Improve the cutlass gemm

* Add gemm_conv

* Add gemm_grouped_conv

.circleci/config.yml

@@ -7,6 +7,9 @@ parameters:
   nightly_build:
     type: boolean
     default: false
+  test_release:
+    type: boolean
+    default: false
 
 jobs:
   build_documentation:
@@ -78,23 +81,24 @@ jobs:
             export DEBIAN_FRONTEND=noninteractive
             export NEEDRESTART_MODE=a
             sudo apt-get update
-            sudo apt-get upgrade -y
-            pip install --upgrade cmake
             sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
             sudo apt-get install openmpi-bin openmpi-common libopenmpi-dev
+            curl -LsSf https://astral.sh/uv/install.sh | sh
       - run:
           name: Install Python package
           command: |
-            pip install -e ".[dev]"
+            uv venv
+            uv pip install cmake
+            uv pip install -e ".[dev]" -v
       - run:
           name: Generate package stubs
           command: |
-            echo "stubs"
-            pip install typing_extensions
-            python setup.py generate_stubs
+            uv pip install typing_extensions
+            uv run --no-project setup.py generate_stubs
       - run:
           name: Run Python tests
           command: |
+            source .venv/bin/activate
             python -m unittest discover python/tests -v
             mpirun --bind-to none -host localhost:8 -np 8 python python/tests/mpi_test_distributed.py
             mlx.launch --verbose -n 8 python/tests/ring_test_distributed.py -v 2> >(tee -a stderr.log >&2)
@@ -102,6 +106,7 @@ jobs:
       - run:
           name: Build CPP only
           command: |
+            source .venv/bin/activate
             mkdir -p build && cd build
             cmake .. -DMLX_BUILD_METAL=OFF -DCMAKE_BUILD_TYPE=DEBUG
             make -j `nproc`
@@ -127,33 +132,30 @@ jobs:
       - run:
           name: Install dependencies
           command: |
-            brew install python@3.9
-            brew install openmpi
-            python3.9 -m venv env
-            source env/bin/activate
-            pip install --upgrade pip
-            pip install --upgrade cmake
-            pip install nanobind==2.4.0
-            pip install numpy
-            pip install torch
-            pip install tensorflow
-            pip install unittest-xml-reporting
+            HOMEBREW_NO_AUTO_UPDATE=1 HOMEBREW_NO_INSTALL_CLEANUP=1 \
+              brew install openmpi uv
       - run:
           name: Install Python package
           command: |
-            source env/bin/activate
+            uv venv --python 3.9
+            uv pip install \
+              nanobind==2.4.0 \
+              cmake \
+              numpy \
+              torch \
+              tensorflow \
+              unittest-xml-reporting
             DEBUG=1 CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
-              pip install -e . -v
+              uv pip install -e . -v
       - run:
           name: Generate package stubs
           command: |
-            source env/bin/activate
-            pip install typing_extensions
-            python setup.py generate_stubs
+            uv pip install typing_extensions
+            uv run --no-project setup.py generate_stubs
       - run:
           name: Run Python tests
           command: |
-            source env/bin/activate
+            source .venv/bin/activate
             LOW_MEMORY=1 DEVICE=cpu python -m xmlrunner discover -v python/tests -o test-results/cpu
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 python -m xmlrunner discover -v python/tests -o test-results/gpu
             mpirun --bind-to none -host localhost:8 -np 8 -x DYLD_LIBRARY_PATH=/opt/homebrew/lib/ python python/tests/mpi_test_distributed.py
@@ -162,16 +164,17 @@ jobs:
       - run:
           name: Build example extension
           command: |
-            source env/bin/activate
+            source .venv/bin/activate
             cd examples/extensions
-            pip install -r requirements.txt
-            python setup.py build_ext -j8
+            uv pip install -r requirements.txt
+            uv run --no-project setup.py build_ext --inplace
+            uv run --no-project python test.py
       - store_test_results:
           path: test-results
       - run:
           name: Build CPP only
           command: |
-            source env/bin/activate
+            source .venv/bin/activate
             mkdir -p build && cd build && cmake .. && make -j `sysctl -n hw.ncpu`
       - run:
           name: Run CPP tests
@@ -180,7 +183,7 @@ jobs:
       - run:
           name: Build small binary
           command: |
-            source env/bin/activate
+            source .venv/bin/activate
             cd build/
             cmake .. -DCMAKE_BUILD_TYPE=MinSizeRel \
               -DBUILD_SHARED_LIBS=ON \
@@ -192,34 +195,60 @@ jobs:
       - run:
           name: Run Python tests with JIT
           command: |
-            source env/bin/activate
             CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
-              pip install -e . -v
+              uv pip install -e .
             LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 \
               METAL_DEBUG_ERROR_MODE=0 \
-              python -m xmlrunner discover -v python/tests -o test-results/gpu_jit
+              uv run --no-project python -m xmlrunner discover \
+                -v python/tests \
+                -o test-results/gpu_jit
 
   cuda_build_and_test:
+    parameters:
+      image_date:
+        type: string
+        default: "2023.11.1"
     machine:
-      image: linux-cuda-12:2023.11.1
+      image: "linux-cuda-12:<< parameters.image_date >>"
       resource_class: gpu.nvidia.small.gen2
     steps:
       - checkout
+      - restore_cache:
+          keys:
+            - cuda-<< parameters.image_date >>-{{ arch }}-
+      - run:
+          name: Install dependencies
+          command: |
+            sudo apt-get update
+            sudo apt-get install libcudnn9-dev-cuda-12
+            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
+            curl -sL https://github.com/ccache/ccache/releases/download/v4.11.3/ccache-4.11.3-linux-x86_64.tar.xz | tar xJf -
+            sudo mv ccache-4.11.3-linux-x86_64/ccache /usr/bin/ccache
+            rm -rf ccache-4.11.3-linux-x86_64
+            curl -LsSf https://astral.sh/uv/install.sh | sh
       - run:
           name: Install Python package
           command: |
-            sudo apt-get update
-            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            python3 -m venv env
-            source env/bin/activate
+            uv venv
             CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
-              pip install -e ".[dev]"
+              uv pip install -e ".[dev]" -v
       - run:
           name: Run Python tests
           command: |
-            source env/bin/activate
+            source .venv/bin/activate
             LOW_MEMORY=1 DEVICE=cpu python -m unittest discover python/tests -v
             LOW_MEMORY=1 DEVICE=gpu python -m tests discover python/tests -v
+      - run:
+          name: CCache report
+          command: |
+            ccache --show-stats
+            ccache --zero-stats
+            ccache --max-size 400MB
+            ccache --cleanup
+      - save_cache:
+          key: cuda-<< parameters.image_date >>-{{ arch }}-{{ epoch }}
+          paths:
+            - /home/circleci/.cache/ccache
 
   build_release:
     parameters:
@@ -315,14 +344,10 @@ jobs:
             export DEBIAN_FRONTEND=noninteractive
             export NEEDRESTART_MODE=a
             sudo apt-get update
-            sudo apt-get upgrade -y
             TZ=Etc/UTC sudo apt-get -y install tzdata
-            sudo apt-get install -y apt-utils
-            sudo apt-get install -y software-properties-common
             sudo add-apt-repository -y ppa:deadsnakes/ppa
             sudo apt-get install -y $PYTHON $PYTHON-dev $PYTHON-full
             sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install -y build-essential git
             $PYTHON -m venv env
             source env/bin/activate
             pip install --upgrade pip
@@ -366,22 +391,27 @@ jobs:
         type: string
         default: ""
     machine:
-      image: linux-cuda-12:default
-      resource_class: gpu.nvidia.small.gen2
+      image: ubuntu-2204:current
+      resource_class: large
     steps:
       - checkout
       - run:
           name: Build wheel
           command: |
+            export DEBIAN_FRONTEND=noninteractive
+            export NEEDRESTART_MODE=a
+            wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
+            sudo dpkg -i cuda-keyring_1.1-1_all.deb
             sudo apt-get update
+            sudo apt-get install cuda-toolkit-12-9 libcudnn9-dev-cuda-12
             sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
             sudo apt-get install zip
-            python -m venv env
-            source env/bin/activate
             pip install auditwheel
             pip install patchelf
             pip install build
             pip install twine
+            export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
+            export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
             << parameters.build_env >> MLX_BUILD_STAGE=2 \
               CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
               python -m build -w
@@ -392,7 +422,6 @@ jobs:
             - run:
                 name: Upload package
                 command: |
-                  source env/bin/activate
                   twine upload wheelhouse/*.whl
       - store_artifacts:
           path: wheelhouse/
@@ -405,19 +434,24 @@ workflows:
             pattern: "^(?!pull/)[-\\w]+$"
             value: << pipeline.git.branch >>
         - not: << pipeline.parameters.nightly_build >>
+        - not: << pipeline.parameters.test_release >>
     jobs:
       - mac_build_and_test:
           matrix:
             parameters:
               macosx_deployment_target: ["13.5", "14.0"]
       - linux_build_and_test
-      - cuda_build_and_test 
+      - cuda_build_and_test:
+          matrix:
+            parameters:
+              image_date: ["2023.11.1", "2025.05.1"]
       - build_documentation 
 
   build_pypi_release:
     when:
       and:
         - not: << pipeline.parameters.nightly_build >>
+        - not: << pipeline.parameters.test_release >>
     jobs:
       - build_release:
           filters:
@@ -538,6 +572,9 @@ workflows:
           requires: [ hold ]
       - cuda_build_and_test:
           requires: [ hold ]
+          matrix:
+            parameters:
+              image_date: ["2023.11.1", "2025.05.1"]
   nightly_build:
     when:
       and:
@@ -601,3 +638,87 @@ workflows:
             parameters:
               python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
       - build_cuda_release
+
+  build_dev_release:
+    when:
+      and:
+        - equal: [ main, << pipeline.git.branch >> ]
+        - << pipeline.parameters.test_release >>
+    jobs:
+      - build_release:
+          matrix:
+            parameters:
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+              macosx_deployment_target: ["13.5", "14.0", "15.0"]
+              build_env: ["DEV_RELEASE=1"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "DEV_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "DEV_RELEASE=1"
+      - build_linux_release:
+          matrix:
+            parameters:
+              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+              build_env: ["DEV_RELEASE=1"]
+      - build_cuda_release:
+          matrix:
+            parameters:
+              build_env: ["DEV_RELEASE=1"]

CMakeLists.txt

@@ -41,7 +41,9 @@ option(MLX_BUILD_GGUF "Include support for GGUF format" ON)
 option(MLX_BUILD_SAFETENSORS "Include support for safetensors format" ON)
 option(MLX_BUILD_BLAS_FROM_SOURCE "Build OpenBLAS from source code" OFF)
 option(MLX_METAL_JIT "Use JIT compilation for Metal kernels" OFF)
+option(MLX_USE_CCACHE "Use CCache for compilation cache when available" ON)
 option(BUILD_SHARED_LIBS "Build mlx as a shared library" OFF)
+option(USE_SYSTEM_FMT "Use system's provided fmt library" OFF)
 
 # --------------------- Processor tests -------------------------
 message(
@@ -68,6 +70,15 @@ else()
   set(MLX_BUILD_METAL OFF)
 endif()
 
+if(MLX_USE_CCACHE)
+  find_program(CCACHE_PROGRAM ccache)
+  if(CCACHE_PROGRAM)
+    set(CMAKE_C_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
+    set(CMAKE_CXX_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
+    set(CMAKE_CUDA_COMPILER_LAUNCHER "${CCACHE_PROGRAM}")
+  endif()
+endif()
+
 # ----------------------------- Lib -----------------------------
 
 include(FetchContent)
@@ -232,12 +243,16 @@ target_include_directories(
 # Do not add mlx_EXPORTS define for shared library.
 set_target_properties(mlx PROPERTIES DEFINE_SYMBOL "")
 
-FetchContent_Declare(
-  fmt
-  GIT_REPOSITORY https://github.com/fmtlib/fmt.git
-  GIT_TAG 10.2.1
-  EXCLUDE_FROM_ALL)
-FetchContent_MakeAvailable(fmt)
+if(USE_SYSTEM_FMT)
+  find_package(fmt REQUIRED)
+else()
+  FetchContent_Declare(
+    fmt
+    GIT_REPOSITORY https://github.com/fmtlib/fmt.git
+    GIT_TAG 10.2.1
+    EXCLUDE_FROM_ALL)
+  FetchContent_MakeAvailable(fmt)
+endif()
 target_link_libraries(mlx PRIVATE $<BUILD_INTERFACE:fmt::fmt-header-only>)
 
 if(MLX_BUILD_PYTHON_BINDINGS)

README.md

@@ -11,10 +11,10 @@ brought to you by Apple machine learning research.
 
 Some key features of MLX include:
 
- - **Familiar APIs**: MLX has a Python API that closely follows NumPy.  MLX
+ - **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX
    also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and
    [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror
-   the Python API.  MLX has higher-level packages like `mlx.nn` and
+   the Python API. MLX has higher-level packages like `mlx.nn` and
    `mlx.optimizers` with APIs that closely follow PyTorch to simplify building
    more complex models.
 
@@ -68,18 +68,23 @@ in the documentation.
 
 ## Installation
 
-MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run:
+MLX is available on [PyPI](https://pypi.org/project/mlx/). To install MLX on
+macOS, run:
 
-**With `pip`**:
-
-```
+```bash
 pip install mlx
 ```
 
-**With `conda`**:
+To install the CUDA backend on Linux, run:
 
+```bash
+pip install mlx[cuda]
 ```
-conda install -c conda-forge mlx
+
+To install a CPU-only Linux package, run:
+
+```bash
+pip install mlx[cpu]
 ```
 
 Checkout the

docs/requirements.txt

@@ -1,4 +1,5 @@
 sphinx
 breathe
 sphinx-book-theme
+sphinx-copybutton
 mlx

docs/src/conf.py

@@ -18,6 +18,7 @@ release = version
 # -- General configuration ---------------------------------------------------
 
 extensions = [
+    "sphinx_copybutton",
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.intersphinx",

docs/src/dev/custom_metal_kernels.rst

@@ -128,6 +128,7 @@ relying on a copy from ``ensure_row_contiguous``:
       input_names=["inp"],
       output_names=["out"],
       source=source
+      ensure_row_contiguous=False,
   )
 
   def exp_elementwise(a: mx.array):
@@ -138,7 +139,6 @@ relying on a copy from ``ensure_row_contiguous``:
           threadgroup=(256, 1, 1),
           output_shapes=[a.shape],
           output_dtypes=[a.dtype],
-          ensure_row_contiguous=False,
       )
       return outputs[0]
 

docs/src/dev/extensions.rst

@@ -394,14 +394,14 @@ below.
         out.set_data(allocator::malloc(out.nbytes()));
 
         // Resolve name of kernel
-        std::ostringstream kname;
-        kname << "axpby_" << "general_" << type_to_name(out);
+        std::stream kname;
+        kname = "axpby_general_" + type_to_name(out);
 
         // Load the metal library
-        auto lib = d.get_library("mlx_ext");
+        auto lib = d.get_library("mlx_ext", current_binary_dir());
 
         // Make a kernel from this metal library
-        auto kernel = d.get_kernel(kname.str(), lib);
+        auto kernel = d.get_kernel(kname, lib);
 
         // Prepare to encode kernel
         auto& compute_encoder = d.get_command_encoder(s.index);

docs/src/index.rst

@@ -70,6 +70,7 @@ are the CPU and GPU.
    python/fft
    python/linalg
    python/metal
+   python/cuda
    python/memory_management
    python/nn
    python/optimizers

docs/src/install.rst

@@ -13,7 +13,7 @@ silicon computer is
 
     pip install mlx
 
-To install from PyPI you must meet the following requirements:
+To install from PyPI your system must meet the following requirements:
 
 - Using an M series chip (Apple silicon)
 - Using a native Python >= 3.9
@@ -26,12 +26,21 @@ To install from PyPI you must meet the following requirements:
 CUDA
 ^^^^
 
-MLX has a CUDA backend which you can use on any Linux platform with CUDA 12
-and SM 7.0 (Volta) and up. To install MLX with CUDA support, run:
+MLX has a CUDA backend which you can install with:
 
 .. code-block:: shell
 
-    pip install "mlx[cuda]"
+    pip install mlx[cuda]
+
+To install the CUDA package from PyPi your system must meet the following
+requirements:
+
+- Nvidia architecture >= SM 7.0 (Volta)
+- Nvidia driver >= 550.54.14
+- CUDA toolkit >= 12.0
+- Linux distribution with glibc >= 2.35
+- Python >= 3.9
+
 
 CPU-only (Linux)
 ^^^^^^^^^^^^^^^^
@@ -40,7 +49,14 @@ For a CPU-only version of MLX that runs on Linux use:
 
 .. code-block:: shell
 
-    pip install "mlx[cpu]"
+    pip install mlx[cpu]
+
+To install the CPU-only package from PyPi your system must meet the following
+requirements:
+
+- Linux distribution with glibc >= 2.35
+- Python >= 3.9
+
 
 Troubleshooting
 ^^^^^^^^^^^^^^^

docs/src/python/cuda.rst

@@ -0,0 +1,9 @@
+CUDA
+=====
+
+.. currentmodule:: mlx.core.cuda
+
+.. autosummary::
+  :toctree: _autosummary
+
+  is_available

docs/src/python/fast.rst

@@ -13,3 +13,4 @@ Fast
   rope
   scaled_dot_product_attention
   metal_kernel
+  cuda_kernel

docs/src/python/optimizers.rst

@@ -51,14 +51,14 @@ the saved state. Here's a simple example:
    optimizer.update(model, grads)
 
    # Save the state
-   state = tree_flatten(optimizer.state)
-   mx.save_safetensors("optimizer.safetensors", dict(state))
+   state = tree_flatten(optimizer.state, destination={})
+   mx.save_safetensors("optimizer.safetensors", state)
 
    # Later on, for example when loading from a checkpoint,
    # recreate the optimizer and load the state
    optimizer = optim.Adam(learning_rate=1e-2)
 
-   state = tree_unflatten(list(mx.load("optimizer.safetensors").items()))
+   state = tree_unflatten(mx.load("optimizer.safetensors"))
    optimizer.state = state
 
 Note, not every optimizer configuation parameter is saved in the state. For

docs/src/usage/compile.rst

@@ -225,7 +225,7 @@ In some cases returning updated state can be pretty inconvenient. Hence,
   def fun(x, y):
       z = x + y
       state.append(z)
-      return mx.exp(z), state
+      return mx.exp(z)
 
   fun(mx.array(1.0), mx.array(2.0))
   # Prints [array(3, dtype=float32)]

docs/src/usage/export.rst

@@ -7,17 +7,17 @@ Exporting Functions
 
 MLX has an API to export and import functions to and from a file. This lets you
 run computations written in one MLX front-end (e.g. Python) in another MLX
-front-end (e.g. C++). 
+front-end (e.g. C++).
 
 This guide walks through the basics of the MLX export API with some examples.
 To see the full list of functions check-out the :ref:`API documentation
 <export>`.
 
-Basics of Exporting 
+Basics of Exporting
 -------------------
 
 Let's start with a simple example:
- 
+
 .. code-block:: python
 
   def fun(x, y):
@@ -67,7 +67,7 @@ specified as variable positional arguments or as a tuple of arrays:
 
   x = mx.array(1.0)
   y = mx.array(1.0)
-   
+
   # Both arguments to fun are positional
   mx.export_function("add.mlxfn", fun, x, y)
 
@@ -133,7 +133,7 @@ parameters are also saved to the ``model.mlxfn`` file.
    For enclosed arrays inside an exported function, be extra careful to ensure
    they are evaluated. The computation graph that gets exported will include
    the computation that produces enclosed inputs.
-  
+
    If the above example was missing ``mx.eval(model.parameters()``, the
    exported function would include the random initialization of the
    :obj:`mlx.nn.Module` parameters.
@@ -150,8 +150,8 @@ parameters, pass them as inputs to the ``call`` wrapper:
      # Set the model's parameters to the input parameters
      model.update(tree_unflatten(list(params.items())))
      return model(x)
- 
-   params = dict(tree_flatten(model.parameters()))
+
+   params = tree_flatten(model.parameters(), destination={})
    mx.export_function("model.mlxfn", call, (mx.zeros(4),), params)
 
 
@@ -169,8 +169,8 @@ to export a function which can be used for inputs with variable shapes:
 
   # Ok
   out, = imported_abs(mx.array(-1.0))
-  
-  # Also ok 
+
+  # Also ok
   out, = imported_abs(mx.array([-1.0, -2.0]))
 
 With ``shapeless=False`` (which is the default), the second call to
@@ -197,7 +197,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   def fun(x, y=None):
       constant = mx.array(3.0)
       if y is not None:
-        x += y 
+        x += y
       return x + constant
 
   with mx.exporter("fun.mlxfn", fun) as exporter:
@@ -215,7 +215,7 @@ a single file by creating an exporting context manager with :func:`exporter`:
   print(out)
 
 In the above example the function constant data, (i.e. ``constant``), is only
-saved once. 
+saved once.
 
 Transformations with Imported Functions
 ---------------------------------------
@@ -238,7 +238,7 @@ on imported functions just like regular Python functions:
   # Prints: array(1, dtype=float32)
   print(dfdx(x))
 
-  # Compile the imported function 
+  # Compile the imported function
   mx.compile(imported_fun)
   # Prints: array(0, dtype=float32)
   print(compiled_fun(x)[0])
@@ -275,7 +275,7 @@ Import and run the function in C++ with only a few lines of code:
   // Prints: array(2, dtype=float32)
   std::cout << outputs[0] << std::endl;
 
-Imported functions can be transformed in C++ just like in Python. Use 
+Imported functions can be transformed in C++ just like in Python. Use
 ``std::vector<mx::array>`` for positional arguments and ``std::map<std::string,
 mx::array>`` for keyword arguments when calling imported functions in C++.
 

examples/extensions/axpby/axpby.cpp

@@ -1,5 +1,6 @@
 // Copyright © 2023-2025 Apple Inc.
 
+#include <dlfcn.h>
 #include <iostream>
 #include <sstream>
 
@@ -16,6 +17,19 @@
 
 namespace my_ext {
 
+// A helper function to find the location of the current binary on disk.
+// The Metal library ("mlx_ext.mtllib"), should be in the same directory.
+std::string current_binary_dir() {
+  static std::string binary_dir = []() {
+    Dl_info info;
+    if (!dladdr(reinterpret_cast<void*>(&current_binary_dir), &info)) {
+      throw std::runtime_error("Unable to get current binary dir.");
+    }
+    return std::filesystem::path(info.dli_fname).parent_path().string();
+  }();
+  return binary_dir;
+}
+
 ///////////////////////////////////////////////////////////////////////////////
 // Operation Implementation
 ///////////////////////////////////////////////////////////////////////////////
@@ -167,16 +181,15 @@ void Axpby::eval_gpu(
   }
 
   // Resolve name of kernel (corresponds to axpby.metal)
-  std::ostringstream kname;
-  kname << "axpby_";
-  kname << (contiguous_kernel ? "contiguous_" : "general_");
-  kname << type_to_name(out);
+  std::string kname = "axpby_";
+  kname += (contiguous_kernel ? "contiguous_" : "general_");
+  kname += type_to_name(out);
 
   // Load the metal library
-  auto lib = d.get_library("mlx_ext");
+  auto lib = d.get_library("mlx_ext", current_binary_dir());
 
   // Make a kernel from this metal library
-  auto kernel = d.get_kernel(kname.str(), lib);
+  auto kernel = d.get_kernel(kname, lib);
 
   // Prepare to encode kernel
   auto& compute_encoder = d.get_command_encoder(s.index);

examples/extensions/requirements.txt

@@ -1,4 +1,4 @@
 setuptools>=42
 cmake>=3.25
 mlx>=0.21.0
-nanobind==2.2.0
+nanobind==2.4.0

examples/extensions/test.py

@@ -3,8 +3,10 @@ from mlx_sample_extensions import axpby
 
 a = mx.ones((3, 4))
 b = mx.ones((3, 4))
-c = axpby(a, b, 4.0, 2.0, stream=mx.cpu)
+c_cpu = axpby(a, b, 4.0, 2.0, stream=mx.cpu)
+c_gpu = axpby(a, b, 4.0, 2.0, stream=mx.gpu)
 
-print(f"c shape: {c.shape}")
-print(f"c dtype: {c.dtype}")
-print(f"c correct: {mx.all(c == 6.0).item()}")
+print(f"c shape: {c_cpu.shape}")
+print(f"c dtype: {c_cpu.dtype}")
+print(f"c_cpu correct: {mx.all(c_cpu == 6.0).item()}")
+print(f"c_gpu correct: {mx.all(c_gpu == 6.0).item()}")

mlx/array.h

@@ -10,6 +10,7 @@
 #include "mlx/allocator.h"
 #include "mlx/dtype.h"
 #include "mlx/event.h"
+#include "mlx/small_vector.h"
 
 namespace mlx::core {
 
@@ -18,8 +19,8 @@ class Primitive;
 
 using Deleter = std::function<void(allocator::Buffer)>;
 using ShapeElem = int32_t;
-using Shape = std::vector<ShapeElem>;
-using Strides = std::vector<int64_t>;
+using Shape = SmallVector<ShapeElem>;
+using Strides = SmallVector<int64_t>;
 
 class array {
   /* An array is really a node in a graph. It contains a shared ArrayDesc

mlx/backend/common/utils.h

@@ -197,7 +197,7 @@ void shared_buffer_reshape(
     array& out);
 
 template <typename T>
-inline std::vector<T> remove_index(std::vector<T> vec, size_t index) {
+inline SmallVector<T> remove_index(SmallVector<T> vec, size_t index) {
   vec.erase(std::next(vec.begin(), index));
   return vec;
 }

mlx/backend/cpu/compiled.cpp

@@ -157,10 +157,12 @@ inline void build_kernel(
 #endif
 
   // Start the kernel
-  os << "void " << kernel_name << "(void** args) {" << std::endl;
+  os << "void " << kernel_name
+     << "(int* shape, int64_t** strides, void** args) {" << std::endl;
 
   // Add the input arguments
   int cnt = 0;
+  int strides_index = 1;
   for (size_t i = 0; i < inputs.size(); ++i) {
     // Skip constants from the input list
     if (is_constant(i)) {
@@ -175,8 +177,8 @@ inline void build_kernel(
        << "];" << std::endl;
     // Scalars and contiguous need no strides
     if (!is_scalar(x) && !contiguous) {
-      os << "  const size_t* " << xname << "_strides = (size_t*)args[" << cnt++
-         << "];" << std::endl;
+      os << "  const int64_t* " << xname << "_strides = strides["
+         << strides_index++ << "];" << std::endl;
     }
   }
 
@@ -186,10 +188,8 @@ inline void build_kernel(
     os << "  " << tstr << "* " << namer.get_name(x) << " = (" << tstr
        << "*)args[" << cnt++ << "];" << std::endl;
   }
-  // Add output strides and shape to extract the indices.
-  if (!contiguous) {
-    os << "  const int* shape = (int*)args[" << cnt++ << "];" << std::endl;
-  } else {
+  // Add output size
+  if (contiguous) {
     os << "  const size_t size = (size_t)args[" << cnt++ << "];" << std::endl;
   }
 
@@ -290,7 +290,6 @@ void Compiled::eval_cpu(
 
   // Collect function input arguments.
   std::vector<void*> args;
-  int strides_index = 1;
   for (size_t i = 0; i < inputs.size(); ++i) {
     if (is_constant_(i)) {
       continue;
@@ -298,9 +297,6 @@ void Compiled::eval_cpu(
     const auto& x = inputs[i];
     encoder.set_input_array(x);
     args.push_back((void*)x.data<void>());
-    if (!contiguous && !is_scalar(x)) {
-      args.push_back(strides[strides_index++].data());
-    }
   }
 
   // Get the kernel name from the lib
@@ -335,16 +331,20 @@ void Compiled::eval_cpu(
     args.push_back(x.data<void>());
     encoder.set_output_array(x);
   }
-  if (!contiguous) {
-    args.push_back((void*)shape.data());
-  } else {
+  if (contiguous) {
     args.push_back((void*)outputs[0].data_size());
   }
-  auto fun = (void (*)(void**))fn_ptr;
+  auto fun = reinterpret_cast<void (*)(int*, int64_t**, void**)>(fn_ptr);
   encoder.dispatch([fun,
                     args = std::move(args),
                     strides = std::move(strides),
-                    shape = std::move(shape)]() mutable { fun(args.data()); });
+                    shape = std::move(shape)]() mutable {
+    SmallVector<int64_t*> strides_ptrs;
+    for (auto& s : strides) {
+      strides_ptrs.push_back(s.data());
+    }
+    fun(shape.data(), strides_ptrs.data(), args.data());
+  });
 }
 
 } // namespace mlx::core

mlx/backend/cpu/copy.cpp

@@ -377,4 +377,10 @@ void copy_cpu_inplace(
       });
 }
 
+array contiguous_copy_cpu(const array& arr, Stream stream) {
+  array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
+  copy_cpu(arr, arr_copy, CopyType::General, stream);
+  return arr_copy;
+}
+
 } // namespace mlx::core

mlx/backend/cpu/copy.h

@@ -30,4 +30,7 @@ void copy_cpu_inplace(
     const std::optional<array>& dynamic_i_offset = std::nullopt,
     const std::optional<array>& dynamic_o_offset = std::nullopt);
 
+// Return a contiguous array with same shape that copies the data of |arr|.
+array contiguous_copy_cpu(const array& arr, Stream stream);
+
 } // namespace mlx::core

mlx/backend/cpu/distributed.cpp

@@ -13,9 +13,7 @@ std::pair<array, bool> ensure_row_contiguous(const array& arr, Stream stream) {
   if (arr.flags().row_contiguous) {
     return {arr, false};
   } else {
-    array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-    copy_cpu(arr, arr_copy, CopyType::General, stream);
-    return {arr_copy, true};
+    return {contiguous_copy_cpu(arr, stream), true};
   }
 };
 
@@ -34,8 +32,7 @@ void AllReduce::eval_cpu(
       }
       return in;
     } else {
-      array arr_copy(in.shape(), in.dtype(), nullptr, {});
-      copy_cpu(in, arr_copy, CopyType::General, s);
+      array arr_copy = contiguous_copy_cpu(in, s);
       out.copy_shared_buffer(arr_copy);
       return arr_copy;
     }

mlx/backend/cpu/jit_compiler.cpp

@@ -2,6 +2,7 @@
 
 #include "mlx/backend/cpu/jit_compiler.h"
 
+#include <algorithm>
 #include <sstream>
 #include <vector>
 

mlx/backend/cpu/lapack.h

@@ -47,7 +47,7 @@ INSTANTIATE_LAPACK_REAL(orgqr)
 INSTANTIATE_LAPACK_REAL(syevd)
 INSTANTIATE_LAPACK_REAL(geev)
 INSTANTIATE_LAPACK_REAL(potrf)
-INSTANTIATE_LAPACK_REAL(gesvdx)
+INSTANTIATE_LAPACK_REAL(gesdd)
 INSTANTIATE_LAPACK_REAL(getrf)
 INSTANTIATE_LAPACK_REAL(getri)
 INSTANTIATE_LAPACK_REAL(trtri)

mlx/backend/cpu/logsumexp.cpp

@@ -87,8 +87,7 @@ void LogSumExp::eval_cpu(const std::vector<array>& inputs, array& out) {
     if (x.flags().contiguous && x.strides()[x.ndim() - 1] == 1) {
       return x;
     } else {
-      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
-      copy_cpu(x, x_copy, CopyType::General, s);
+      array x_copy = contiguous_copy_cpu(x, s);
       encoder.add_temporary(x_copy);
       return x_copy;
     }

mlx/backend/cpu/masked_mm.cpp

@@ -136,9 +136,8 @@ void BlockMaskedMM::eval_cpu(const std::vector<array>& inputs, array& out) {
           }
           return std::make_tuple(true, sty, arr, false);
         } else {
-          array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-          copy_cpu(arr, arr_copy, CopyType::General, s);
           int64_t stx = arr.shape(-1);
+          array arr_copy = contiguous_copy_cpu(arr, s);
           return std::make_tuple(false, stx, arr_copy, true);
         }
       };

mlx/backend/cpu/quantized.cpp

@@ -712,9 +712,7 @@ void fast::AffineQuantize::eval_cpu(
     if (arr.flags().row_contiguous) {
       return std::make_pair(arr, false);
     } else {
-      array arr_copy(arr.shape(), arr.dtype(), nullptr, {});
-      copy_cpu(arr, arr_copy, CopyType::General, s);
-      return std::make_pair(arr_copy, true);
+      return std::make_pair(contiguous_copy_cpu(arr, s), true);
     }
   };
 

mlx/backend/cpu/reduce.cpp

@@ -491,19 +491,27 @@ void Reduce::eval_cpu(const std::vector<array>& inputs, array& out) {
         switch (in.dtype()) {
           case bool_:
           case uint8:
+            reduce_dispatch_sum_prod<uint8_t>(in, out, reduce_type_, axes_);
+            break;
+          case uint16:
+            reduce_dispatch_sum_prod<uint16_t>(in, out, reduce_type_, axes_);
+            break;
+          case uint32:
+            reduce_dispatch_sum_prod<uint32_t>(in, out, reduce_type_, axes_);
+            break;
+          case uint64:
+            reduce_dispatch_sum_prod<uint64_t>(in, out, reduce_type_, axes_);
+            break;
           case int8:
             reduce_dispatch_sum_prod<int8_t>(in, out, reduce_type_, axes_);
             break;
           case int16:
-          case uint16:
             reduce_dispatch_sum_prod<int16_t>(in, out, reduce_type_, axes_);
             break;
           case int32:
-          case uint32:
             reduce_dispatch_sum_prod<int32_t>(in, out, reduce_type_, axes_);
             break;
           case int64:
-          case uint64:
             reduce_dispatch_sum_prod<int64_t>(in, out, reduce_type_, axes_);
             break;
           case float16:

mlx/backend/cpu/scan.cpp

@@ -250,10 +250,8 @@ void Scan::eval_cpu(const std::vector<array>& inputs, array& out) {
   // Ensure contiguity
   auto in = inputs[0];
   if (!in.flags().row_contiguous) {
-    array arr_copy(in.shape(), in.dtype(), nullptr, {});
-    copy_cpu(in, arr_copy, CopyType::General, stream());
-    in = arr_copy;
-    encoder.add_temporary(arr_copy);
+    in = contiguous_copy_cpu(in, stream());
+    encoder.add_temporary(in);
   }
   out.set_data(allocator::malloc(out.nbytes()));
 

mlx/backend/cpu/softmax.cpp

@@ -131,8 +131,7 @@ void Softmax::eval_cpu(const std::vector<array>& inputs, array& out) {
       }
       return x;
     } else {
-      array x_copy(x.shape(), x.dtype(), nullptr, {});
-      copy_cpu(x, x_copy, CopyType::General, s);
+      array x_copy = contiguous_copy_cpu(x, s);
       out.copy_shared_buffer(x_copy);
       return x_copy;
     }

mlx/backend/cpu/sort.cpp

@@ -8,7 +8,7 @@
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
-
+#include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
 
 namespace mlx::core {
@@ -333,47 +333,24 @@ void Sort::eval_cpu(const std::vector<array>& inputs, array& out) {
   assert(inputs.size() == 1);
   auto& in = inputs[0];
 
+  int axis = axis_;
+  if (axis < 0) {
+    axis += in.ndim();
+  }
+
   // Copy input to output
-  CopyType ctype = (in.flags().contiguous && in.strides()[axis_] != 0)
+  CopyType ctype = (in.flags().contiguous && in.strides()[axis] != 0)
       ? CopyType::Vector
       : CopyType::General;
   copy_cpu(in, out, ctype, stream());
 
   auto& encoder = cpu::get_command_encoder(stream());
   encoder.set_output_array(out);
-  encoder.dispatch(
-      [out = array::unsafe_weak_copy(out), axis_ = axis_]() mutable {
-        switch (out.dtype()) {
-          case bool_:
-            return sort<bool>(out, axis_);
-          case uint8:
-            return sort<uint8_t>(out, axis_);
-          case uint16:
-            return sort<uint16_t>(out, axis_);
-          case uint32:
-            return sort<uint32_t>(out, axis_);
-          case uint64:
-            return sort<uint64_t>(out, axis_);
-          case int8:
-            return sort<int8_t>(out, axis_);
-          case int16:
-            return sort<int16_t>(out, axis_);
-          case int32:
-            return sort<int32_t>(out, axis_);
-          case int64:
-            return sort<int64_t>(out, axis_);
-          case float32:
-            return sort<float>(out, axis_);
-          case float64:
-            return sort<double>(out, axis_);
-          case float16:
-            return sort<float16_t>(out, axis_);
-          case bfloat16:
-            return sort<bfloat16_t>(out, axis_);
-          case complex64:
-            return sort<complex64_t>(out, axis_);
-        }
-      });
+  encoder.dispatch([out = array::unsafe_weak_copy(out), axis]() mutable {
+    dispatch_all_types(out.dtype(), [&](auto type_tag) {
+      sort<MLX_GET_TYPE(type_tag)>(out, axis);
+    });
+  });
 }
 
 void ArgPartition::eval_cpu(const std::vector<array>& inputs, array& out) {

mlx/backend/cpu/svd.cpp

@@ -81,9 +81,7 @@ void svd_impl(
     // Vᵀ of shape N x N. (M x M in lapack).
     const int ldvt = M;
 
-    auto job_u = (u_ptr) ? "V" : "N";
-    auto job_vt = (u_ptr) ? "V" : "N";
-    static constexpr auto range = "A";
+    auto jobz = (u_ptr) ? "A" : "N";
 
     // Will contain the number of singular values after the call has returned.
     int ns = 0;
@@ -91,30 +89,20 @@ void svd_impl(
 
     // Will contain the indices of eigenvectors that failed to converge (not
     // used here but required by lapack).
-    auto iwork = array::Data{allocator::malloc(sizeof(int) * 12 * K)};
+    auto iwork = array::Data{allocator::malloc(sizeof(int) * 8 * K)};
 
     static const int lwork_query = -1;
 
-    static const int ignored_int = 0;
-    static const T ignored_float = 0;
-
     int info;
 
     // Compute workspace size.
-    gesvdx<T>(
-        /* jobu = */ job_u,
-        /* jobvt = */ job_vt,
-        /* range = */ range,
+    gesdd<T>(
+        /* jobz = */ jobz,
         // M and N are swapped since lapack expects column-major.
         /* m = */ &N,
         /* n = */ &M,
         /* a = */ nullptr,
         /* lda = */ &lda,
-        /* vl = */ &ignored_float,
-        /* vu = */ &ignored_float,
-        /* il = */ &ignored_int,
-        /* iu = */ &ignored_int,
-        /* ns = */ &ns,
         /* s = */ nullptr,
         /* u = */ nullptr,
         /* ldu = */ &ldu,
@@ -136,20 +124,13 @@ void svd_impl(
 
     // Loop over matrices.
     for (int i = 0; i < num_matrices; i++) {
-      gesvdx<T>(
-          /* jobu = */ job_u,
-          /* jobvt = */ job_vt,
-          /* range = */ range,
+      gesdd<T>(
+          /* jobz = */ jobz,
           // M and N are swapped since lapack expects column-major.
           /* m = */ &N,
           /* n = */ &M,
           /* a = */ in_ptr + M * N * i,
           /* lda = */ &lda,
-          /* vl = */ &ignored_float,
-          /* vu = */ &ignored_float,
-          /* il = */ &ignored_int,
-          /* iu = */ &ignored_int,
-          /* ns = */ &ns,
           /* s = */ s_ptr + K * i,
           // According to the identity above, lapack will write Vᵀᵀ as U.
           /* u = */ vt_ptr ? vt_ptr + N * N * i : nullptr,
@@ -167,13 +148,6 @@ void svd_impl(
         ss << "svd_impl: sgesvdx_ failed with code " << info;
         throw std::runtime_error(ss.str());
       }
-
-      if (ns != K) {
-        std::stringstream ss;
-        ss << "svd_impl: expected " << K << " singular values, but " << ns
-           << " were computed.";
-        throw std::runtime_error(ss.str());
-      }
     }
   });
   encoder.add_temporary(in);

mlx/backend/cuda/CMakeLists.txt

@@ -6,8 +6,8 @@
 target_sources(
   mlx
   PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/allocator.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/arange.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/arg_reduce.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/binary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/binary_two.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/compiled.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/copy.cu
@@ -15,18 +15,27 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_dynamic.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/copy/copy_general_input.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/conv.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/conv/gemm_conv.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/conv/gemm_grouped_conv.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/cuda.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/cudnn_utils.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/custom_kernel.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/device.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/gemv.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cutlass_gemm.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/simple_gemm.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/jit_module.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/indexing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/kernel_utils.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/matmul.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/layer_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/logsumexp.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/primitives.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/random.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/all_reduce.cu
@@ -35,16 +44,28 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/row_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rms_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/rope.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/scaled_dot_product_attention.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/scan.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/ternary.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/unary.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/utils.cpp
-          ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/affine_quantize.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/quantized.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/worker.cpp)
 
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/binary)
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/unary)
+
+if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9.0)
+  target_sources(
+    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_9.cu)
+else()
+  target_sources(
+    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_0.cpp)
+endif()
+
 target_compile_definitions(mlx PRIVATE MLX_USE_CUDA)
 
 # Embed kernel sources in binary for JIT compilation.
@@ -69,6 +90,9 @@ target_include_directories(mlx PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/gen")
 target_compile_options(mlx
                        PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>")
 
+# Keep ptx around for inspection
+target_compile_options(mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--keep>")
+
 # Enable calling host constexpr functions from device. This is needed because
 # the constexpr version of isnan is host only.
 target_compile_options(
@@ -87,11 +111,18 @@ endif()
 target_compile_options(
   mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--Wno-deprecated-gpu-targets>")
 
-# Compute capability 7 is required for synchronization between CPU/GPU with
-# managed memory. TODO: Add more architectures for potential performance gain.
-set(MLX_CUDA_ARCHITECTURES
-    "70;80"
-    CACHE STRING "CUDA architectures")
+# Use stronger binaries compression. This feature was introduced in CUDA 12.8
+# and requires drivers released after CUDA 12.4.
+if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8.0)
+  target_compile_options(
+    mlx PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:--compress-mode=size>")
+endif()
+
+# Compute capability >= 7.0 is required for synchronization between CPU/GPU with
+# managed memory.
+if(NOT DEFINED MLX_CUDA_ARCHITECTURES)
+  set(MLX_CUDA_ARCHITECTURES "native")
+endif()
 message(STATUS "CUDA architectures: ${MLX_CUDA_ARCHITECTURES}")
 set_target_properties(mlx PROPERTIES CUDA_ARCHITECTURES
                                      "${MLX_CUDA_ARCHITECTURES}")
@@ -123,6 +154,23 @@ target_link_libraries(mlx PRIVATE CUDA::cublasLt)
 # Use NVRTC and driver APIs.
 target_link_libraries(mlx PRIVATE CUDA::nvrtc CUDA::cuda_driver)
 
+# Use the frontend APIs of cuDNN.
+FetchContent_Declare(
+  cudnn
+  GIT_REPOSITORY https://github.com/NVIDIA/cudnn-frontend.git
+  GIT_TAG v1.14.0
+  GIT_SHALLOW TRUE
+  EXCLUDE_FROM_ALL)
+set(CUDNN_FRONTEND_SKIP_JSON_LIB ON)
+set(CUDNN_FRONTEND_BUILD_SAMPLES OFF)
+set(CUDNN_FRONTEND_BUILD_TESTS OFF)
+set(CUDNN_FRONTEND_BUILD_PYTHON_BINDINGS OFF)
+FetchContent_MakeAvailable(cudnn)
+target_link_libraries(mlx PRIVATE cudnn_frontend)
+# Link with the actual cuDNN libraries.
+include(${cudnn_frontend_SOURCE_DIR}/cmake/cuDNN.cmake)
+target_link_libraries(mlx PRIVATE CUDNN::cudnn_all)
+
 # Suppress nvcc warnings on MLX headers.
 target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
                                    --diag_suppress=997>)
@@ -130,3 +178,12 @@ target_compile_options(mlx PRIVATE $<$<COMPILE_LANGUAGE:CUDA>:-Xcudafe
 # Install CCCL headers for JIT.
 install(DIRECTORY ${cccl_SOURCE_DIR}/include/cuda
         DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/cccl)
+
+# Fetch and make available cutlass
+FetchContent_Declare(
+  cutlass
+  GIT_REPOSITORY https://github.com/NVIDIA/cutlass.git
+  GIT_TAG v4.1.0)
+FetchContent_Populate(cutlass)
+target_include_directories(
+  mlx PRIVATE $<BUILD_INTERFACE:${cutlass_SOURCE_DIR}/include>)

mlx/backend/cuda/allocator.cpp

@@ -2,7 +2,6 @@
 
 #include "mlx/backend/cuda/allocator.h"
 #include "mlx/backend/cuda/utils.h"
-#include "mlx/backend/cuda/worker.h"
 #include "mlx/utils.h"
 
 #include <cuda_runtime.h>
@@ -17,14 +16,66 @@ namespace cu {
 
 constexpr int page_size = 16384;
 
+// Any allocations smaller than this will try to use the small pool
+constexpr int small_block_size = 8;
+
+// The small pool size in bytes. This should be a multiple of the host page
+// size and small_block_size.
+constexpr int small_pool_size = 4 * page_size;
+
+SmallSizePool::SmallSizePool() {
+  auto num_blocks = small_pool_size / small_block_size;
+  buffer_ = new Block[num_blocks];
+
+  next_free_ = buffer_;
+
+  CHECK_CUDA_ERROR(cudaMallocManaged(&data_, small_pool_size));
+  CHECK_CUDA_ERROR(
+      cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetReadMostly, 0));
+
+  auto curr = next_free_;
+  for (size_t i = 1; i < num_blocks; ++i) {
+    curr->next = buffer_ + i;
+    curr = curr->next;
+  }
+  curr->next = nullptr;
+}
+
+SmallSizePool::~SmallSizePool() {
+  CHECK_CUDA_ERROR(cudaFree(data_));
+  delete[] buffer_;
+}
+
+CudaBuffer* SmallSizePool::malloc() {
+  if (next_free_ == nullptr) {
+    return nullptr;
+  }
+  Block* b = next_free_;
+  uint64_t i = next_free_ - buffer_;
+  next_free_ = next_free_->next;
+  b->buf.data = static_cast<char*>(data_) + i * small_block_size;
+  b->buf.size = small_block_size;
+  return &b->buf;
+}
+
+void SmallSizePool::free(CudaBuffer* buf) {
+  auto b = reinterpret_cast<Block*>(buf);
+  b->next = next_free_;
+  next_free_ = b;
+}
+
+bool SmallSizePool::in_pool(CudaBuffer* buf) {
+  constexpr int num_blocks = (small_pool_size / small_block_size);
+  auto b = reinterpret_cast<Block*>(buf);
+  int64_t block_num = b - buffer_;
+  return block_num >= 0 && block_num < num_blocks;
+}
+
 CudaAllocator::CudaAllocator()
     : buffer_cache_(
           page_size,
           [](CudaBuffer* buf) { return buf->size; },
-          [this](CudaBuffer* buf) {
-            cuda_free(buf->data);
-            delete buf;
-          }) {
+          [this](CudaBuffer* buf) { cuda_free(buf); }) {
   // TODO: Set memory limit for multi-device.
   size_t free, total;
   CHECK_CUDA_ERROR(cudaMemGetInfo(&free, &total));
@@ -36,7 +87,9 @@ Buffer CudaAllocator::malloc(size_t size) {
   // Find available buffer from cache.
   auto orig_size = size;
   std::unique_lock lock(mutex_);
-  if (size < page_size) {
+  if (size <= small_block_size) {
+    size = 8;
+  } else if (size < page_size) {
     size = next_power_of_2(size);
   } else {
     size = page_size * ((size + page_size - 1) / page_size);
@@ -44,19 +97,25 @@ Buffer CudaAllocator::malloc(size_t size) {
 
   CudaBuffer* buf = buffer_cache_.reuse_from_cache(size);
   if (!buf) {
-    // If we have a lot of memory pressure or are over the maximum cache size,
-    // try to reclaim memory from the cache.
-    size_t mem_required = get_active_memory() + get_cache_memory() + size;
-    if (mem_required >= memory_limit_) {
-      buffer_cache_.release_cached_buffers(mem_required - memory_limit_);
+    // If we have a lot of memory pressure try to reclaim memory from the cache.
+    int64_t mem_to_free =
+        get_active_memory() + get_cache_memory() + size - memory_limit_;
+    if (mem_to_free > 0) {
+      buffer_cache_.release_cached_buffers(mem_to_free);
     }
 
+    // Try the scalar pool first
+    if (size <= small_block_size) {
+      buf = scalar_pool_.malloc();
+    }
     lock.unlock();
-    buf = new CudaBuffer{nullptr, size};
-    cudaError_t err = cudaMallocManaged(&buf->data, size);
-    if (err != cudaSuccess && err != cudaErrorMemoryAllocation) {
-      throw std::runtime_error(fmt::format(
-          "cudaMallocManaged failed: {}.", cudaGetErrorString(err)));
+    if (!buf) {
+      buf = new CudaBuffer{nullptr, size};
+      cudaError_t err = cudaMallocManaged(&buf->data, size);
+      if (err != cudaSuccess && err != cudaErrorMemoryAllocation) {
+        throw std::runtime_error(fmt::format(
+            "cudaMallocManaged failed: {}.", cudaGetErrorString(err)));
+      }
     }
     lock.lock();
   }
@@ -67,7 +126,6 @@ Buffer CudaAllocator::malloc(size_t size) {
   if (get_cache_memory() > max_pool_size_) {
     buffer_cache_.release_cached_buffers(get_cache_memory() - max_pool_size_);
   }
-
   return Buffer{buf};
 }
 
@@ -82,9 +140,7 @@ void CudaAllocator::free(Buffer buffer) {
   if (get_cache_memory() < max_pool_size_) {
     buffer_cache_.recycle_to_cache(buf);
   } else {
-    lock.unlock();
-    cuda_free(buf->data);
-    delete buf;
+    cuda_free(buf);
   }
 }
 
@@ -96,27 +152,14 @@ size_t CudaAllocator::size(Buffer buffer) const {
   return buf->size;
 }
 
-void CudaAllocator::register_this_thread() {
-  std::lock_guard lock(worker_mutex_);
-  allowed_threads_.insert(std::this_thread::get_id());
-}
-
-void CudaAllocator::cuda_free(void* buf) {
-  // If cuda_free() is called from a unregistered thread, reschedule the call to
-  // worker.
-  {
-    std::lock_guard lock(worker_mutex_);
-    if (allowed_threads_.count(std::this_thread::get_id()) == 0) {
-      if (!worker_) {
-        worker_.reset(new Worker);
-      }
-      worker_->add_task([this, buf]() { this->cuda_free(buf); });
-      worker_->end_batch();
-      worker_->commit();
-      return;
-    }
+// This must be called with mutex_ aquired
+void CudaAllocator::cuda_free(CudaBuffer* buf) {
+  if (scalar_pool_.in_pool(buf)) {
+    scalar_pool_.free(buf);
+  } else {
+    cudaFree(buf->data);
+    delete buf;
   }
-  cudaFree(buf);
 }
 
 size_t CudaAllocator::get_active_memory() const {

mlx/backend/cuda/allocator.h

@@ -7,13 +7,10 @@
 
 #include <mutex>
 #include <set>
-#include <thread>
 #include <utility>
 
 namespace mlx::core::cu {
 
-class Worker;
-
 using allocator::Buffer;
 
 // Stores cuda-managed unified memory.
@@ -22,21 +19,35 @@ struct CudaBuffer {
   size_t size;
 };
 
+class SmallSizePool {
+ private:
+  union Block {
+    Block* next;
+    CudaBuffer buf;
+  };
+
+  Block* buffer_{nullptr};
+  void* data_{nullptr};
+  Block* next_free_{nullptr};
+
+ public:
+  SmallSizePool();
+  ~SmallSizePool();
+
+  SmallSizePool(const SmallSizePool&) = delete;
+  SmallSizePool& operator=(const SmallSizePool&) = delete;
+
+  CudaBuffer* malloc();
+  void free(CudaBuffer* buf);
+  bool in_pool(CudaBuffer* buf);
+};
+
 class CudaAllocator : public allocator::Allocator {
  public:
   Buffer malloc(size_t size) override;
   void free(Buffer buffer) override;
   size_t size(Buffer buffer) const override;
 
-  // Register current thread as safe to free buffers.
-  // In cuda freeing a buffer implicitly synchronizes stream, and for threads
-  // that may be waited by gpu stream (for example cpu stream threads), freeing
-  // buffers there would result in dead lock.
-  void register_this_thread();
-
-  // Call cudaFree in the safe thread.
-  void cuda_free(void* buf);
-
   size_t get_active_memory() const;
   size_t get_peak_memory() const;
   void reset_peak_memory();
@@ -47,19 +58,18 @@ class CudaAllocator : public allocator::Allocator {
   void clear_cache();
 
  private:
+  void cuda_free(CudaBuffer* buf);
+
   CudaAllocator();
   friend CudaAllocator& allocator();
 
-  std::mutex worker_mutex_;
-  std::unique_ptr<Worker> worker_;
-  std::set<std::thread::id> allowed_threads_;
-
   std::mutex mutex_;
   size_t memory_limit_;
   size_t max_pool_size_;
   BufferCache<CudaBuffer> buffer_cache_;
   size_t active_memory_{0};
   size_t peak_memory_{0};
+  SmallSizePool scalar_pool_;
 };
 
 CudaAllocator& allocator();

mlx/backend/cuda/arange.cu

@@ -0,0 +1,55 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/device/fp16_math.cuh"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/dtype_utils.h"
+#include "mlx/primitives.h"
+
+#include <nvtx3/nvtx3.hpp>
+#include <thrust/device_ptr.h>
+#include <thrust/transform.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+template <typename T>
+struct Arange {
+  const T start;
+  const T step;
+
+  __device__ T operator()(uint32_t i) const {
+    return start + i * step;
+  }
+};
+
+} // namespace cu
+
+void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Arange::eval_gpu");
+  if (out.size() == 0) {
+    return;
+  }
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  auto& encoder = cu::get_command_encoder(stream());
+  encoder.set_output_array(out);
+
+  auto capture = encoder.capture_context();
+  dispatch_int_float_types(out.dtype(), "Arange", [&](auto type_tag) {
+    using CTYPE = MLX_GET_TYPE(type_tag);
+    using OutType = cuda_type_t<CTYPE>;
+    CTYPE step =
+        static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_);
+    thrust::transform(
+        cu::thrust_policy(encoder.stream()),
+        thrust::counting_iterator<uint32_t>(0),
+        thrust::counting_iterator<uint32_t>(out.data_size()),
+        thrust::device_pointer_cast(out.data<OutType>()),
+        cu::Arange<OutType>{
+            static_cast<OutType>(start_), static_cast<OutType>(step)});
+  });
+}
+
+} // namespace mlx::core

mlx/backend/cuda/arg_reduce.cu

@@ -1,8 +1,8 @@
 // Copyright © 2025 Apple Inc.
+
 #include "mlx/backend/common/utils.h"
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/device/fp16_math.cuh"
-#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"
@@ -44,8 +44,11 @@ struct ArgMin {
   }
 
   template <int N>
-  __device__ IndexValPair<T>
-  reduce_many(IndexValPair<T> best, T (&vals)[N], uint32_t offset) {
+  __device__ IndexValPair<T> reduce_many(
+      IndexValPair<T> best,
+      const AlignedVector<T, N>& vals,
+      uint32_t offset) {
+#pragma unroll
     for (int i = 0; i < N; i++) {
       if (vals[i] < best.val) {
         best.val = vals[i];
@@ -74,8 +77,11 @@ struct ArgMax {
   }
 
   template <int N>
-  __device__ IndexValPair<T>
-  reduce_many(IndexValPair<T> best, T (&vals)[N], uint32_t offset) {
+  __device__ IndexValPair<T> reduce_many(
+      IndexValPair<T> best,
+      const AlignedVector<T, N>& vals,
+      uint32_t offset) {
+#pragma unroll
     for (int i = 0; i < N; i++) {
       if (vals[i] > best.val) {
         best.val = vals[i];
@@ -106,16 +112,15 @@ __global__ void arg_reduce_general(
 
   int64_t in_idx = elem_to_loc(index, shape.data(), in_strides.data(), ndim);
   int64_t out_idx = elem_to_loc(index, shape.data(), out_strides.data(), ndim);
+  in += in_idx;
 
   Op op;
   T init = op.init();
   IndexValPair<T> best{0, init};
 
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    T vals[N_READS];
     auto tid = r * BLOCK_DIM + block.thread_index().x;
-    cub::LoadDirectBlocked(
-        tid, strided_iterator(in + in_idx, axis_stride), vals, axis_size, init);
+    auto vals = load_vector<N_READS>(in, tid, axis_size, axis_stride, init);
     best = op.reduce_many(best, vals, tid * N_READS);
   }
 
@@ -166,6 +171,7 @@ void ArgReduce::eval_gpu(const std::vector<array>& inputs, array& out) {
           kernel,
           num_blocks,
           block_dim(),
+          0,
           in.data<T>(),
           out.data<uint32_t>(),
           out.size(),

mlx/backend/cuda/binary/CMakeLists.txt

@@ -0,0 +1,21 @@
+target_sources(
+  mlx
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/add.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/arctan2.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/bitwise_binary.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/divide.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/equal.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/greater.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/greater_equal.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/less.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/less_equal.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/logical_and.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/logical_or.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/log_add_exp.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/minimum.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/maximum.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/multiply.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/power.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/remainder.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/not_equal.cu
+  PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/subtract.cu)

mlx/backend/cuda/binary/add.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Add)
+} // namespace mlx::core

mlx/backend/cuda/binary/arctan2.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(ArcTan2)
+} // namespace mlx::core

mlx/backend/cuda/binary/binary.cuh

@@ -28,7 +28,7 @@ __global__ void binary_ss(const In* a, const In* b, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = Op{}(a[0], b[0]);
+      out_vec[i] = Op{}(a[0], b[0]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -49,7 +49,7 @@ __global__ void binary_sv(const In* a, const In* b, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = Op{}(a[0], b_vec.val[i]);
+      out_vec[i] = Op{}(a[0], b_vec[i]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -70,7 +70,7 @@ __global__ void binary_vs(const In* a, const In* b, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = Op{}(a_vec.val[i], b[0]);
+      out_vec[i] = Op{}(a_vec[i], b[0]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -92,46 +92,96 @@ __global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = Op{}(a_vec.val[i], b_vec.val[i]);
+      out_vec[i] = Op{}(a_vec[i], b_vec[i]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
   }
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
+template <
+    typename Op,
+    typename In,
+    typename Out,
+    typename IdxT,
+    int NDIM,
+    int N_READS>
 __global__ void binary_g_nd(
     const In* a,
     const In* b,
     Out* out,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
-        index, shape.data(), a_strides.data(), b_strides.data());
-    out[index] = Op{}(a[a_idx], b[b_idx]);
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[NDIM - 1];
+  auto a_stride_x = a_strides[NDIM - 1];
+  auto b_stride_x = b_strides[NDIM - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
+      index_rest * shape_x, shape.data(), a_strides.data(), b_strides.data());
+  auto a_vec =
+      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
+  auto b_vec =
+      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
+
+  AlignedVector<Out, N_READS> out_vec;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    out_vec[i] = Op{}(a_vec[i], b_vec[i]);
+  }
+  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
-template <typename Op, typename In, typename Out, typename IdxT>
+template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
 __global__ void binary_g(
     const In* a,
     const In* b,
     Out* out,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides a_strides,
     const __grid_constant__ Strides b_strides,
     int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc_4d(
-        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
-    out[index] = Op{}(a[a_idx], b[b_idx]);
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[ndim - 1];
+  auto a_stride_x = a_strides[ndim - 1];
+  auto b_stride_x = b_strides[ndim - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [a_idx, b_idx] = elem_to_loc(
+      index_rest * shape_x,
+      shape.data(),
+      a_strides.data(),
+      b_strides.data(),
+      ndim);
+  auto a_vec =
+      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
+  auto b_vec =
+      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
+
+  AlignedVector<Out, N_READS> out_vec;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    out_vec[i] = Op{}(a_vec[i], b_vec[i]);
+  }
+  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
 template <typename Op, typename In, typename Out>
@@ -209,36 +259,61 @@ void binary_op_gpu_inplace(
                 auto& a_strides = strides[0];
                 auto& b_strides = strides[1];
                 int ndim = shape.size();
+                int work_per_thread = 1;
+                auto dim0 = ndim > 0 ? shape.back() : 1;
+                auto rest = out.size() / dim0;
+                if (dim0 >= 4) {
+                  work_per_thread = 4;
+                }
+                dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
+                auto block_dims = get_block_dims(dim0, rest, 1);
+                uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
+                uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                    auto kernel = cu::
-                        binary_g_nd<Op, InType, OutType, IdxT, dims_constant()>;
-                    auto [num_blocks, block_dims] =
-                        get_launch_args(kernel, out, large());
+                    auto kernel = cu::binary_g_nd<
+                        Op,
+                        InType,
+                        OutType,
+                        IdxT,
+                        dims_constant(),
+                        1>;
+                    if (work_per_thread == 4) {
+                      kernel = cu::binary_g_nd<
+                          Op,
+                          InType,
+                          OutType,
+                          IdxT,
+                          dims_constant(),
+                          4>;
+                    }
                     encoder.add_kernel_node(
                         kernel,
-                        num_blocks,
+                        {num_blocks_x, num_blocks_y},
                         block_dims,
+                        0,
                         a.data<InType>(),
                         b.data<InType>(),
                         out.data<OutType>(),
-                        out.size(),
+                        rest,
                         const_param<dims_constant()>(shape),
                         const_param<dims_constant()>(a_strides),
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
-                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
-                  auto [num_blocks, block_dims] =
-                      get_launch_args(kernel, out, large());
+                  auto kernel = cu::binary_g<Op, InType, OutType, IdxT, 1>;
+                  if (work_per_thread == 4) {
+                    kernel = cu::binary_g<Op, InType, OutType, IdxT, 4>;
+                  }
                   encoder.add_kernel_node(
                       kernel,
-                      num_blocks,
+                      {num_blocks_x, num_blocks_y},
                       block_dims,
+                      0,
                       a.data<InType>(),
                       b.data<InType>(),
                       out.data<OutType>(),
-                      out.size(),
+                      rest,
                       const_param(shape),
                       const_param(a_strides),
                       const_param(b_strides),
@@ -248,8 +323,7 @@ void binary_op_gpu_inplace(
         } else {
           dispatch_bool(out.data_size() > UINT32_MAX, [&](auto large) {
             using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            // TODO: Choose optimized value based on type size.
-            constexpr int N_READS = 4;
+            constexpr int N_READS = 16 / sizeof(InType);
             auto kernel = cu::binary_ss<Op, InType, OutType, IdxT, N_READS>;
             if (bopt == BinaryOpType::ScalarVector) {
               kernel = cu::binary_sv<Op, InType, OutType, IdxT, N_READS>;
@@ -259,16 +333,12 @@ void binary_op_gpu_inplace(
               kernel = cu::binary_vv<Op, InType, OutType, IdxT, N_READS>;
             }
             auto [num_blocks, block_dims] = get_launch_args(
-                kernel,
-                out.data_size(),
-                out.shape(),
-                out.strides(),
-                large(),
-                N_READS);
+                out.data_size(), out.shape(), out.strides(), large(), N_READS);
             encoder.add_kernel_node(
                 kernel,
                 num_blocks,
                 block_dims,
+                0,
                 a.data<InType>(),
                 b.data<InType>(),
                 out.data<OutType>(),
@@ -306,54 +376,4 @@ void binary_op_gpu(
     binary_op_gpu<cu::func>(inputs, out, name(), s);                  \
   }
 
-BINARY_GPU(Add)
-BINARY_GPU(ArcTan2)
-BINARY_GPU(Divide)
-BINARY_GPU(Remainder)
-BINARY_GPU(Greater)
-BINARY_GPU(GreaterEqual)
-BINARY_GPU(Less)
-BINARY_GPU(LessEqual)
-BINARY_GPU(LogicalAnd)
-BINARY_GPU(LogicalOr)
-BINARY_GPU(LogAddExp)
-BINARY_GPU(Maximum)
-BINARY_GPU(Minimum)
-BINARY_GPU(Multiply)
-BINARY_GPU(NotEqual)
-BINARY_GPU(Power)
-BINARY_GPU(Subtract)
-
-void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("Equal::eval_gpu");
-  auto& s = out.primitive().stream();
-  if (equal_nan_) {
-    binary_op_gpu<cu::NaNEqual>(inputs, out, name(), s);
-  } else {
-    binary_op_gpu<cu::Equal>(inputs, out, name(), s);
-  }
-}
-
-void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
-  nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
-  auto& s = out.primitive().stream();
-  switch (op_) {
-    case BitwiseBinary::And:
-      binary_op_gpu<cu::BitwiseAnd>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::Or:
-      binary_op_gpu<cu::BitwiseOr>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::Xor:
-      binary_op_gpu<cu::BitwiseXor>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::LeftShift:
-      binary_op_gpu<cu::LeftShift>(inputs, out, name(), s);
-      break;
-    case BitwiseBinary::RightShift:
-      binary_op_gpu<cu::RightShift>(inputs, out, name(), s);
-      break;
-  }
-}
-
 } // namespace mlx::core

mlx/backend/cuda/binary/bitwise_binary.cu

@@ -0,0 +1,27 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
+  auto& s = out.primitive().stream();
+  switch (op_) {
+    case BitwiseBinary::And:
+      binary_op_gpu<cu::BitwiseAnd>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::Or:
+      binary_op_gpu<cu::BitwiseOr>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::Xor:
+      binary_op_gpu<cu::BitwiseXor>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::LeftShift:
+      binary_op_gpu<cu::LeftShift>(inputs, out, name(), s);
+      break;
+    case BitwiseBinary::RightShift:
+      binary_op_gpu<cu::RightShift>(inputs, out, name(), s);
+      break;
+  }
+}
+} // namespace mlx::core

mlx/backend/cuda/binary/divide.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Divide)
+} // namespace mlx::core

mlx/backend/cuda/binary/equal.cu

@@ -0,0 +1,15 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+void Equal::eval_gpu(const std::vector<array>& inputs, array& out) {
+  nvtx3::scoped_range r("Equal::eval_gpu");
+  auto& s = out.primitive().stream();
+  if (equal_nan_) {
+    binary_op_gpu<cu::NaNEqual>(inputs, out, name(), s);
+  } else {
+    binary_op_gpu<cu::Equal>(inputs, out, name(), s);
+  }
+}
+} // namespace mlx::core

mlx/backend/cuda/binary/greater.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Greater)
+} // namespace mlx::core

mlx/backend/cuda/binary/greater_equal.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(GreaterEqual)
+} // namespace mlx::core

mlx/backend/cuda/binary/less.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Less)
+} // namespace mlx::core

mlx/backend/cuda/binary/less_equal.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(LessEqual)
+} // namespace mlx::core

mlx/backend/cuda/binary/log_add_exp.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(LogAddExp)
+} // namespace mlx::core

mlx/backend/cuda/binary/logical_and.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(LogicalAnd)
+} // namespace mlx::core

mlx/backend/cuda/binary/logical_or.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(LogicalOr)
+} // namespace mlx::core

mlx/backend/cuda/binary/maximum.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Maximum)
+} // namespace mlx::core

mlx/backend/cuda/binary/minimum.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Minimum)
+} // namespace mlx::core

mlx/backend/cuda/binary/multiply.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Multiply)
+} // namespace mlx::core

mlx/backend/cuda/binary/not_equal.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(NotEqual)
+} // namespace mlx::core

mlx/backend/cuda/binary/power.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Power)
+} // namespace mlx::core

mlx/backend/cuda/binary/remainder.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Remainder)
+} // namespace mlx::core

mlx/backend/cuda/binary/subtract.cu

@@ -0,0 +1,7 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/binary/binary.cuh"
+
+namespace mlx::core {
+BINARY_GPU(Subtract)
+} // namespace mlx::core

mlx/backend/cuda/binary_two.cu

@@ -33,8 +33,8 @@ binary_two_ss(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
       auto out = Op{}(a[0], b[0]);
-      out_a_vec.val[i] = out[0];
-      out_b_vec.val[i] = out[1];
+      out_a_vec[i] = out[0];
+      out_b_vec[i] = out[1];
     }
 
     store_vector<N_READS>(out_a, index, out_a_vec);
@@ -60,9 +60,9 @@ binary_two_sv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
     AlignedVector<Out, N_READS> out_b_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      auto out = Op{}(a[0], b_vec.val[i]);
-      out_a_vec.val[i] = out[0];
-      out_b_vec.val[i] = out[1];
+      auto out = Op{}(a[0], b_vec[i]);
+      out_a_vec[i] = out[0];
+      out_b_vec[i] = out[1];
     }
 
     store_vector<N_READS>(out_a, index, out_a_vec);
@@ -88,9 +88,9 @@ binary_two_vs(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
     AlignedVector<Out, N_READS> out_b_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      auto out = Op{}(a_vec.val[i], b[0]);
-      out_a_vec.val[i] = out[0];
-      out_b_vec.val[i] = out[1];
+      auto out = Op{}(a_vec[i], b[0]);
+      out_a_vec[i] = out[0];
+      out_b_vec[i] = out[1];
     }
 
     store_vector<N_READS>(out_a, index, out_a_vec);
@@ -117,9 +117,9 @@ binary_two_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
     AlignedVector<Out, N_READS> out_b_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      auto out = Op{}(a_vec.val[i], b_vec.val[i]);
-      out_a_vec.val[i] = out[0];
-      out_b_vec.val[i] = out[1];
+      auto out = Op{}(a_vec[i], b_vec[i]);
+      out_a_vec[i] = out[0];
+      out_b_vec[i] = out[1];
     }
 
     store_vector<N_READS>(out_a, index, out_a_vec);
@@ -127,45 +127,99 @@ binary_two_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
   }
 }
 
-template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
+template <
+    typename Op,
+    typename In,
+    typename Out,
+    typename IdxT,
+    int NDIM,
+    int N_READS>
 __global__ void binary_two_g_nd(
     const In* a,
     const In* b,
     Out* out_a,
     Out* out_b,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
-        index, shape.data(), a_strides.data(), b_strides.data());
-    auto out = Op{}(a[a_idx], b[b_idx]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[NDIM - 1];
+  auto a_stride_x = a_strides[NDIM - 1];
+  auto b_stride_x = b_strides[NDIM - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
+      index_rest * shape_x, shape.data(), a_strides.data(), b_strides.data());
+  auto a_vec =
+      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
+  auto b_vec =
+      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
+
+  AlignedVector<Out, N_READS> out_vec_a;
+  AlignedVector<Out, N_READS> out_vec_b;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    auto out = Op{}(a_vec[i], b_vec[i]);
+    out_vec_a[i] = out[0];
+    out_vec_b[i] = out[1];
+  }
+  store_vector(out_a + shape_x * index_rest, index_x, out_vec_a, shape_x);
+  store_vector(out_b + shape_x * index_rest, index_x, out_vec_b, shape_x);
 }
 
-template <typename Op, typename In, typename Out, typename IdxT>
+template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
 __global__ void binary_two_g(
     const In* a,
     const In* b,
     Out* out_a,
     Out* out_b,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides a_strides,
     const __grid_constant__ Strides b_strides,
     int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [a_idx, b_idx] = elem_to_loc_4d(
-        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
-    auto out = Op{}(a[a_idx], b[b_idx]);
-    out_a[index] = out[0];
-    out_b[index] = out[1];
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[ndim - 1];
+  auto a_stride_x = a_strides[ndim - 1];
+  auto b_stride_x = b_strides[ndim - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [a_idx, b_idx] = elem_to_loc(
+      index_rest * shape_x,
+      shape.data(),
+      a_strides.data(),
+      b_strides.data(),
+      ndim);
+  auto a_vec =
+      load_vector<N_READS>(a + a_idx, index_x, shape_x, a_stride_x, In(0));
+  auto b_vec =
+      load_vector<N_READS>(b + b_idx, index_x, shape_x, b_stride_x, In(0));
+
+  AlignedVector<Out, N_READS> out_vec_a;
+  AlignedVector<Out, N_READS> out_vec_b;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    auto out = Op{}(a_vec[i], b_vec[i]);
+    out_vec_a[i] = out[0];
+    out_vec_b[i] = out[1];
+  }
+  store_vector(out_a + shape_x * index_rest, index_x, out_vec_a, shape_x);
+  store_vector(out_b + shape_x * index_rest, index_x, out_vec_b, shape_x);
 }
 
 template <typename Op, typename In, typename Out>
@@ -225,6 +279,17 @@ void binary_two_op_gpu_inplace(
                 auto& a_strides = strides[0];
                 auto& b_strides = strides[1];
                 int ndim = shape.size();
+                int work_per_thread = 1;
+                auto dim0 = ndim > 0 ? shape.back() : 1;
+                auto rest = out_a.size() / dim0;
+                if (dim0 >= 4) {
+                  work_per_thread = 4;
+                }
+                dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
+                auto block_dims = get_block_dims(dim0, rest, 1);
+                uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
+                uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
+
                 if (ndim <= 3) {
                   dispatch_1_2_3(ndim, [&](auto dims_constant) {
                     auto kernel = cu::binary_two_g_nd<
@@ -232,35 +297,46 @@ void binary_two_op_gpu_inplace(
                         InType,
                         OutType,
                         IdxT,
-                        dims_constant()>;
-                    auto [num_blocks, block_dims] =
-                        get_launch_args(kernel, out_a, large());
+                        dims_constant(),
+                        1>;
+                    if (work_per_thread == 4) {
+                      kernel = cu::binary_two_g_nd<
+                          Op,
+                          InType,
+                          OutType,
+                          IdxT,
+                          dims_constant(),
+                          4>;
+                    }
                     encoder.add_kernel_node(
                         kernel,
-                        num_blocks,
+                        {num_blocks_x, num_blocks_y},
                         block_dims,
+                        0,
                         a.data<InType>(),
                         b.data<InType>(),
                         out_a.data<OutType>(),
                         out_b.data<OutType>(),
-                        out_a.size(),
+                        rest,
                         const_param<dims_constant()>(shape),
                         const_param<dims_constant()>(a_strides),
                         const_param<dims_constant()>(b_strides));
                   });
                 } else {
-                  auto kernel = cu::binary_two_g<Op, InType, OutType, IdxT>;
-                  auto [num_blocks, block_dims] =
-                      get_launch_args(kernel, out_a, large());
+                  auto kernel = cu::binary_two_g<Op, InType, OutType, IdxT, 1>;
+                  if (work_per_thread == 4) {
+                    kernel = cu::binary_two_g<Op, InType, OutType, IdxT, 4>;
+                  }
                   encoder.add_kernel_node(
                       kernel,
-                      num_blocks,
+                      {num_blocks_x, num_blocks_y},
                       block_dims,
+                      0,
                       a.data<InType>(),
                       b.data<InType>(),
                       out_a.data<OutType>(),
                       out_b.data<OutType>(),
-                      out_a.size(),
+                      rest,
                       const_param(shape),
                       const_param(a_strides),
                       const_param(b_strides),
@@ -270,8 +346,7 @@ void binary_two_op_gpu_inplace(
         } else {
           dispatch_bool(out_a.data_size() > UINT32_MAX, [&](auto large) {
             using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-            // TODO: Choose optimized value based on type size.
-            constexpr int N_READS = 4;
+            constexpr int N_READS = 16 / sizeof(InType);
             auto kernel = cu::binary_two_ss<Op, InType, OutType, IdxT, N_READS>;
             if (bopt == BinaryOpType::ScalarVector) {
               kernel = cu::binary_two_sv<Op, InType, OutType, IdxT, N_READS>;
@@ -281,7 +356,6 @@ void binary_two_op_gpu_inplace(
               kernel = cu::binary_two_vv<Op, InType, OutType, IdxT, N_READS>;
             }
             auto [num_blocks, block_dims] = get_launch_args(
-                kernel,
                 out_a.data_size(),
                 out_a.shape(),
                 out_a.strides(),
@@ -291,6 +365,7 @@ void binary_two_op_gpu_inplace(
                 kernel,
                 num_blocks,
                 block_dims,
+                0,
                 a.data<InType>(),
                 b.data<InType>(),
                 out_a.data<OutType>(),

mlx/backend/cuda/compiled.cpp

@@ -104,10 +104,41 @@ struct FusedKernelBuilder {
           "  }\n";
     }
 
+    // Vectorized read loop
+    if (contiguous) {
+      for (size_t i = 0; i < inputs.size(); ++i) {
+        const auto& x = inputs[i];
+        if (is_scalar(x) || is_constant(i)) {
+          continue;
+        }
+        const std::string& xname = namer.get_name(x);
+        std::string type = dtype_to_cuda_type(x.dtype());
+        os += fmt::format(
+            "  auto vec_{0} = load_vector<work_per_thread, {1}>({0} + index, 0, size - index, 0);\n",
+            xname,
+            type);
+      }
+    }
+
+    // Create some space for the outputs
+    for (const auto& x : outputs) {
+      const std::string& xname = namer.get_name(x);
+      std::string type = dtype_to_cuda_type(x.dtype());
+      os += fmt::format(
+          "  AlignedVector<{}, work_per_thread> vec_{};\n", type, xname);
+    }
+
     // Work loop
-    os +=
-        "\n"
-        "  for (int i = 0; i < work_per_thread && index < size; i++) {\n";
+    if (!contiguous) {
+      os +=
+          "\n"
+          "  for (int i = 0; i < work_per_thread && index < size; i++) {\n";
+    } else {
+      os +=
+          "\n"
+          "  #pragma unroll\n"
+          "  for (int i = 0; i < work_per_thread; i++) {\n";
+    }
 
     // Read inputs.
     for (size_t i = 0; i < inputs.size(); ++i) {
@@ -122,7 +153,7 @@ struct FusedKernelBuilder {
       } else if (is_scalar(x)) {
         value = fmt::format("{}[0]", xname);
       } else if (contiguous) {
-        value = fmt::format("{}[index]", xname);
+        value = fmt::format("vec_{}[i]", xname);
       } else {
         value = fmt::format("{}[{}_idx]", xname, xname);
       }
@@ -150,25 +181,30 @@ struct FusedKernelBuilder {
 
     // Write output.
     for (const auto& x : outputs) {
-      os += fmt::format("    {0}[index] = tmp_{0};\n", namer.get_name(x));
+      os += fmt::format("    vec_{0}[i] = tmp_{0};\n", namer.get_name(x));
     }
 
     // End of work loop
-    os +=
-        "\n"
-        "    index++;\n";
     if (!contiguous) {
+      os += "\n";
       for (size_t i = 0; i < inputs.size(); ++i) {
         const auto& x = inputs[i];
         const std::string& xname = namer.get_name(x);
         if (is_scalar(x) || is_constant(i)) {
           continue;
         }
-        os += "    " + xname + "_idx += " + xname + "_strides[NDIM - 1];\n";
+        os += fmt::format("    {0}_idx += {0}_strides[NDIM - 1];\n", xname);
       }
     }
     os += "  }\n";
 
+    // Store the output to global memory
+    for (const auto& x : outputs) {
+      os += fmt::format(
+          "  store_vector({0} + index, 0, vec_{0}, size - index);\n",
+          namer.get_name(x));
+    }
+
     os += "}\n";
   }
 };
@@ -192,6 +228,15 @@ void Compiled::eval_gpu(
   nvtx3::scoped_range r("Compiled::eval_gpu");
   auto& s = stream();
 
+  // Determine the work per thread for the vectorized reads/writes. We take it
+  // as 16 over the max itemsize for the outputs. Another heuristic could be
+  // over the max itemsize of all arrays.
+  int max_size = 1;
+  for (const auto& x : outputs) {
+    max_size = (max_size > x.itemsize()) ? max_size : x.itemsize();
+  }
+  int work_per_thread = 16 / max_size;
+
   cu::JitModule& mod = cu::get_jit_module(s.device, lib_name(), [&]() {
     // Build source code.
     cu::FusedKernelBuilder builder{
@@ -205,29 +250,25 @@ void Compiled::eval_gpu(
     builder.os += "\n} // namespace mlx::core::cu\n";
     // Build kernel names.
     std::vector<std::string> kernel_names;
-    for (auto work_per_thread : std::array<int, 2>{1, 4}) {
-      kernel_names.push_back(fmt::format(
-          "mlx::core::cu::{}_contiguous<uint32_t, {}>",
-          lib_name(),
-          work_per_thread));
-      kernel_names.push_back(fmt::format(
-          "mlx::core::cu::{}_contiguous<int64_t, {}>",
-          lib_name(),
-          work_per_thread));
+    kernel_names.push_back(fmt::format(
+        "mlx::core::cu::{}_contiguous<uint32_t, {}>",
+        lib_name(),
+        work_per_thread));
+    kernel_names.push_back(fmt::format(
+        "mlx::core::cu::{}_contiguous<int64_t, {}>",
+        lib_name(),
+        work_per_thread));
+    for (auto wpt : std::array<int, 2>{1, work_per_thread}) {
       for (int i = 1; i <= MAX_NDIM; ++i) {
         kernel_names.push_back(fmt::format(
-            "mlx::core::cu::{}_strided<{}, uint32_t, {}>",
-            lib_name(),
-            i,
-            work_per_thread));
+            "mlx::core::cu::{}_strided<{}, uint32_t, {}>", lib_name(), i, wpt));
         kernel_names.push_back(fmt::format(
-            "mlx::core::cu::{}_strided<{}, int64_t, {}>",
-            lib_name(),
-            i,
-            work_per_thread));
+            "mlx::core::cu::{}_strided<{}, int64_t, {}>", lib_name(), i, wpt));
       }
     }
-    return std::make_pair(std::move(builder.os), std::move(kernel_names));
+
+    return std::make_tuple(
+        false, std::move(builder.os), std::move(kernel_names));
   });
 
   // Collapse contiguous dims to route to a faster kernel if possible. Also
@@ -269,7 +310,6 @@ void Compiled::eval_gpu(
   }
 
   // Choose work per thread
-  int work_per_thread = 4;
   if (!contiguous && shape.back() % work_per_thread != 0) {
     work_per_thread = 1;
   }
@@ -294,8 +334,8 @@ void Compiled::eval_gpu(
 
   auto kernel = mod.get_kernel(kernel_name);
   auto [num_blocks, block_dims] =
-      get_launch_args(kernel, outputs[0], large, work_per_thread);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
+      get_launch_args(outputs[0], large, work_per_thread);
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
 }
 
 } // namespace mlx::core

mlx/backend/cuda/conv.cpp

@@ -0,0 +1,418 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/conv/conv.h"
+#include "mlx/backend/cuda/cudnn_utils.h"
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/lru_cache.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/primitives.h"
+
+#include <nvtx3/nvtx3.hpp>
+
+#include <cassert>
+
+namespace mlx::core {
+
+namespace {
+
+// Alias for better readability.
+#define CONV_FORWARD CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR
+#define CONV_BACKWARD_INPUT \
+  CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR
+#define CONV_BACKWARD_WEIGHT \
+  CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR
+
+// Custom placeholder representing fallback kernel.
+#define CONV_FALLBACK static_cast<cudnnBackendDescriptorType_t>(-1)
+
+struct ConvCacheKey {
+  int device_id;
+  cudnnDataType_t cudnn_dtype;
+  std::array<int, MAX_NDIM> input_shape;
+  std::array<int, MAX_NDIM> weight_shape;
+  std::array<int, MAX_NDIM> stride;
+  std::array<int, MAX_NDIM> padding_lo;
+  std::array<int, MAX_NDIM> padding_hi;
+  std::array<int, MAX_NDIM> dilation;
+  int groups;
+  bool flip;
+  uint8_t input_alignment;
+  uint8_t weight_alignment;
+  uint8_t output_alignment;
+};
+
+auto& conv_cache() {
+  static LRUBytesKeyCache<
+      ConvCacheKey,
+      std::pair<
+          cudnnBackendDescriptorType_t,
+          std::optional<cudnn_frontend::ExecutionPlan>>>
+      cache(/* capacity */ 128);
+  return cache;
+}
+
+auto get_conv_op_settings(
+    cudnnBackendDescriptorType_t backend_type,
+    array& x,
+    array& w,
+    array& y,
+    const std::vector<int>& kernel_strides,
+    const std::vector<int>& padding_lo_,
+    const std::vector<int>& padding_hi_,
+    const std::vector<int>& kernel_dilation,
+    const std::vector<int>& input_dilation) {
+  auto padding_lo = convert_vector<int64_t>(padding_lo_);
+  auto padding_hi = convert_vector<int64_t>(padding_hi_);
+
+  if (backend_type == CONV_BACKWARD_INPUT) {
+    for (int i = 0; i < padding_lo.size(); ++i) {
+      int wt_size = 1 + kernel_dilation[i] * (w.shape(1 + i) - 1);
+      padding_lo[i] = wt_size - padding_lo[i] - 1;
+      int in_size = 1 + kernel_strides[i] * (x.shape(1 + i) - 1);
+      int out_size = 1 + input_dilation[i] * (y.shape(1 + i) - 1);
+      padding_hi[i] = out_size - in_size + padding_hi[i];
+    }
+    return std::make_tuple(
+        convert_vector<int64_t>(input_dilation),
+        std::move(padding_lo),
+        std::move(padding_hi),
+        convert_vector<int64_t>(kernel_dilation));
+
+  } else if (backend_type == CONV_BACKWARD_WEIGHT) {
+    padding_hi = padding_lo;
+    return std::make_tuple(
+        convert_vector<int64_t>(kernel_dilation),
+        std::move(padding_lo),
+        std::move(padding_hi),
+        convert_vector<int64_t>(kernel_strides));
+
+  } else {
+    return std::make_tuple(
+        convert_vector<int64_t>(kernel_strides),
+        std::move(padding_lo),
+        std::move(padding_hi),
+        convert_vector<int64_t>(kernel_dilation));
+  }
+}
+
+std::optional<cudnn_frontend::OperationGraph> build_conv_op_graph(
+    cu::CommandEncoder& encoder,
+    cudnnBackendDescriptorType_t backend_type,
+    Dtype dtype,
+    array& x,
+    array& w,
+    array& y,
+    const SmallVector<int64_t>& stride,
+    const SmallVector<int64_t>& padding_lo,
+    const SmallVector<int64_t>& padding_hi,
+    const SmallVector<int64_t>& dilation) {
+  try {
+    auto compute_dtype = (dtype == float16 || dtype == bfloat16)
+        ? CUDNN_DATA_FLOAT
+        : dtype_to_cudnn_type(dtype);
+    auto conv_desc = cudnn_frontend::ConvDescBuilder()
+                         .setDataType(compute_dtype)
+                         .setMathMode(CUDNN_CROSS_CORRELATION)
+                         .setNDims(stride.size())
+                         .setStrides(stride.size(), stride.data())
+                         .setPrePadding(padding_lo.size(), padding_lo.data())
+                         .setPostPadding(padding_hi.size(), padding_hi.data())
+                         .setDilation(dilation.size(), dilation.data())
+                         .build();
+
+    auto op = cudnn_frontend::OperationBuilder(backend_type)
+                  .setxDesc(build_cudnn_tensor_nchw('x', x))
+                  .setwDesc(build_cudnn_tensor_nchw('w', w))
+                  .setyDesc(build_cudnn_tensor_nchw('y', y))
+                  .setcDesc(conv_desc)
+                  .build();
+
+    std::array<cudnn_frontend::Operation const*, 1> ops = {&op};
+    return cudnn_frontend::OperationGraphBuilder()
+        .setHandle(encoder.device().cudnn_handle())
+        .setOperationGraph(ops.size(), ops.data())
+        .build();
+  } catch (cudnn_frontend::cudnnException& error) {
+    if (error.getCudnnStatus() != CUDNN_STATUS_BAD_PARAM) {
+      throw;
+    }
+    return std::nullopt;
+  }
+}
+
+// Transpose from (C_out, H, W, C_in / groups) to (C_in, H, W, C_out / groups).
+array group_transpose(
+    const array& x,
+    int groups,
+    int group_dim,
+    int axis1,
+    int axis2,
+    Stream s) {
+  if (groups == 1) {
+    return swapaxes_in_eval(x, axis1, axis2);
+  }
+  int ndim = x.ndim();
+  if (group_dim < 0) {
+    group_dim += ndim;
+  }
+  if (axis1 < 0) {
+    axis1 += ndim;
+  }
+  if (axis2 < 0) {
+    axis2 += ndim;
+  }
+  if (group_dim <= axis1) {
+    axis1 += 1;
+  }
+  if (group_dim <= axis2) {
+    axis2 += 1;
+  }
+  auto shape = x.shape();
+  shape.insert(shape.begin() + group_dim, groups);
+  shape[group_dim + 1] = shape[group_dim + 1] / groups;
+  array x_trans = reshape_in_eval(x, std::move(shape), s);
+  x_trans = swapaxes_in_eval(x_trans, axis1, axis2);
+  x_trans = flatten_in_eval(x_trans, group_dim, group_dim + 1, s);
+  return x_trans;
+}
+
+// Do necessary transposes and copies to prepare the inputs and outputs for
+// building the cuDNN conv op. It is safe to be called multiple times in one
+// eval_gpu, with cost of possible redundant copies.
+std::tuple<array, array, array> prepare_args(
+    cu::CommandEncoder& encoder,
+    cudnnBackendDescriptorType_t backend_type,
+    array in,
+    array wt,
+    array out,
+    int groups,
+    Stream s) {
+  // Transpose the args depending on the backend type.
+  // TODO: Handle groups.
+  if (backend_type == CONV_BACKWARD_INPUT) {
+    wt = group_transpose(wt, groups, 0, 0, -1, s);
+  } else if (backend_type == CONV_BACKWARD_WEIGHT) {
+    in = group_transpose(in, groups, -1, 0, -1, s);
+    wt = swapaxes_in_eval(wt, 0, -1);
+    // Create a contiguous array that shares the data with |out|, but with dim
+    // C_in and C_out swapped.
+    Shape shape(out.shape());
+    std::swap(shape.front(), shape.back());
+    Strides strides(shape.size(), 1);
+    for (int i = shape.size() - 2; i >= 0; --i) {
+      strides[i] = shape[i + 1] * strides[i + 1];
+    }
+    array intermediate(std::move(shape), out.dtype(), nullptr, {});
+    intermediate.copy_shared_buffer(
+        out, std::move(strides), {true, true, false}, out.data_size());
+    out = intermediate;
+  }
+
+  // cuDNN requires contiguous input.
+  if (!in.flags().row_contiguous) {
+    in = contiguous_copy_gpu(in, s);
+    encoder.add_temporary(in);
+  }
+  if (!wt.flags().row_contiguous) {
+    wt = contiguous_copy_gpu(wt, s);
+    encoder.add_temporary(wt);
+  }
+
+  return {std::move(in), std::move(wt), std::move(out)};
+}
+
+// Get the x/w/y args from the in/wt/out args depending on backend type.
+inline std::tuple<array&, array&, array&> dispatch_args(
+    cudnnBackendDescriptorType_t backend_type,
+    array& in,
+    array& wt,
+    array& out) {
+  switch (backend_type) {
+    case CONV_BACKWARD_INPUT:
+      return {out, wt, in};
+    case CONV_BACKWARD_WEIGHT:
+      return {in, out, wt};
+    default:
+      return {in, wt, out};
+  }
+}
+
+// Register inputs and outputs before actually running conv op. Can only be
+// called once per eval_gpu.
+void register_args(
+    cu::CommandEncoder& encoder,
+    cudnnBackendDescriptorType_t backend_type,
+    array& in,
+    array& wt,
+    array& intermediate_out,
+    array& final_out) {
+  encoder.set_input_array(in);
+  encoder.set_input_array(wt);
+  encoder.set_output_array(final_out);
+
+  if (backend_type == CONV_BACKWARD_WEIGHT) {
+    // Turn |out| into a strided array, which will have C_in and C_out swapped
+    // in vjp and the final |grad_weight| will then be contiguous.
+    Strides strides = intermediate_out.strides();
+    std::swap(strides.front(), strides.back());
+    final_out.copy_shared_buffer(
+        intermediate_out,
+        std::move(strides),
+        {false, false, false},
+        intermediate_out.data_size());
+  }
+}
+
+} // namespace
+
+void Convolution::eval_gpu(const std::vector<array>& inputs, array& out_) {
+  nvtx3::scoped_range r("Convolution::eval_gpu");
+  if (out_.size() == 0) {
+    return;
+  }
+
+  assert(inputs.size() == 2);
+  array in = inputs[0];
+  array wt = inputs[1];
+  array out = out_;
+  out.set_data(allocator::malloc(out.nbytes()));
+  Dtype dtype = out.dtype();
+
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+
+  // Search cache.
+  ConvCacheKey cache_key{
+      encoder.device().cuda_device(),
+      dtype_to_cudnn_type(dtype),
+      vector_key(in.shape()),
+      vector_key(wt.shape()),
+      vector_key(kernel_strides_),
+      vector_key(padding_lo_),
+      vector_key(padding_hi_),
+      vector_key(kernel_dilation_),
+      groups_,
+      flip_,
+      get_alignment(in),
+      get_alignment(wt),
+      get_alignment(out)};
+  if (auto it = conv_cache().find(cache_key); it != conv_cache().end()) {
+    auto& [backend_type, plan] = it->second;
+    if (plan) {
+      // Run cached plan.
+      std::tie(in, wt, out) =
+          prepare_args(encoder, backend_type, in, wt, out, groups_, s);
+      register_args(encoder, backend_type, in, wt, out, out_);
+      auto [x, w, y] = dispatch_args(backend_type, in, wt, out);
+      if (!encode_cudnn_plan(encoder, *plan, {'x', 'w', 'y'}, x, w, y)) {
+        throw std::runtime_error("[conv] Cached plan failed to execute.");
+      }
+    } else {
+      // Run fallback kernel.
+      gemm_conv(
+          encoder,
+          in,
+          wt,
+          out,
+          kernel_strides_,
+          padding_lo_,
+          kernel_dilation_,
+          input_dilation_,
+          groups_,
+          flip_,
+          s);
+    }
+    return;
+  }
+
+  // There is no reliable way to deduce the proper cuDNN backend for the
+  // convolution, so we make a best guess and then try.
+  SmallVector<cudnnBackendDescriptorType_t, 2> try_backends;
+  if (flip_) {
+    // When weight is flipped, we assume it is backward input convolution.
+    try_backends.push_back(CONV_BACKWARD_INPUT);
+  } else {
+    // Otherwise it could be backward weight convolution or forward convolution,
+    // mathematically there is no difference so we have to use heuristics.
+    // Empirically backward convolutions have large kernel dimensions, and
+    // usually have |in| and |wt| transposed.
+    if (!in.flags().row_contiguous && !wt.flags().row_contiguous &&
+        wt.shape(2) > out.shape(2)) {
+      try_backends = {CONV_BACKWARD_WEIGHT, CONV_FORWARD};
+    } else {
+      try_backends = {CONV_FORWARD, CONV_BACKWARD_WEIGHT};
+    }
+  }
+
+  // Try to build op graph.
+  cudnnBackendDescriptorType_t backend_type;
+  std::optional<cudnn_frontend::OperationGraph> op_graph;
+  for (auto try_backend : try_backends) {
+    auto [in_copy, wt_copy, out_copy] =
+        prepare_args(encoder, try_backend, in, wt, out, groups_, s);
+    auto [x, w, y] = dispatch_args(try_backend, in_copy, wt_copy, out_copy);
+    auto [stride, padding_lo, padding_hi, dilation] = get_conv_op_settings(
+        try_backend,
+        x,
+        w,
+        y,
+        kernel_strides_,
+        padding_lo_,
+        padding_hi_,
+        kernel_dilation_,
+        input_dilation_);
+    op_graph = build_conv_op_graph(
+        encoder,
+        try_backend,
+        dtype,
+        x,
+        w,
+        y,
+        stride,
+        padding_lo,
+        padding_hi,
+        dilation);
+    if (op_graph) {
+      backend_type = try_backend;
+      in = std::move(in_copy);
+      wt = std::move(wt_copy);
+      out = std::move(out_copy);
+      break;
+    }
+  }
+
+  if (op_graph) {
+    // Setup inputs and outputs.
+    register_args(encoder, backend_type, in, wt, out, out_);
+
+    // Find a plan for the graph and execute it.
+    auto plan = find_cudnn_plan_from_op_graph(
+        encoder.device().cudnn_handle(), backend_type, dtype, *op_graph);
+    if (!plan) {
+      throw std::runtime_error("[conv] Unable to find an execution plan.");
+    }
+    auto [x, w, y] = dispatch_args(backend_type, in, wt, out);
+    if (encode_cudnn_plan(encoder, *plan, {'x', 'w', 'y'}, x, w, y)) {
+      conv_cache().emplace(
+          cache_key, std::make_pair(backend_type, std::move(*plan)));
+      return;
+    }
+  }
+
+  // Use fallback kernel for settings not supported by cuDNN.
+  gemm_conv(
+      encoder,
+      in,
+      wt,
+      out,
+      kernel_strides_,
+      padding_lo_,
+      kernel_dilation_,
+      input_dilation_,
+      groups_,
+      flip_,
+      s);
+  conv_cache().emplace(cache_key, std::make_pair(CONV_FALLBACK, std::nullopt));
+}
+
+} // namespace mlx::core

mlx/backend/cuda/conv/conv.h

@@ -0,0 +1,126 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/gpu/copy.h"
+
+namespace mlx::core {
+
+template <int NDIM>
+struct ConvParams {
+  int N; // Batch size
+  int C; // In channels
+  int O; // Out channels
+  int strides[NDIM];
+  int padding[NDIM];
+  int kernel_dilation[NDIM];
+  int input_dilation[NDIM];
+  int groups;
+  bool flip;
+  int in_spatial_dims[NDIM];
+  int wt_spatial_dims[NDIM];
+  int out_spatial_dims[NDIM];
+  int64_t in_strides[NDIM + 2];
+
+  ConvParams(
+      const array& in,
+      const array& wt,
+      const array& out,
+      const std::vector<int>& strides,
+      const std::vector<int>& padding,
+      const std::vector<int>& kernel_dilation,
+      const std::vector<int>& input_dilation,
+      int groups,
+      bool flip)
+      : N(in.shape(0)),
+        C(in.shape(-1)),
+        O(wt.shape(0)),
+        groups(groups),
+        flip(flip) {
+    std::copy_n(strides.begin(), NDIM, this->strides);
+    std::copy_n(padding.begin(), NDIM, this->padding);
+    std::copy_n(kernel_dilation.begin(), NDIM, this->kernel_dilation);
+    std::copy_n(input_dilation.begin(), NDIM, this->input_dilation);
+    std::copy_n(in.shape().begin() + 1, NDIM, this->in_spatial_dims);
+    std::copy_n(wt.shape().begin() + 1, NDIM, this->wt_spatial_dims);
+    std::copy_n(out.shape().begin() + 1, NDIM, this->out_spatial_dims);
+    std::copy_n(in.strides().begin(), NDIM + 2, this->in_strides);
+  }
+};
+
+void gemm_grouped_conv(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    const array& wt,
+    array& out,
+    const std::vector<int>& strides,
+    const std::vector<int>& padding,
+    const std::vector<int>& kernel_dilation,
+    const std::vector<int>& input_dilation,
+    int groups,
+    bool flip,
+    Stream s);
+
+void gemm_conv(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    const array& wt,
+    array& out,
+    const std::vector<int>& strides,
+    const std::vector<int>& padding,
+    const std::vector<int>& kernel_dilation,
+    const std::vector<int>& input_dilation,
+    bool flip,
+    Stream s);
+
+inline void gemm_conv(
+    cu::CommandEncoder& encoder,
+    array in,
+    array wt,
+    array& out,
+    const std::vector<int>& strides,
+    const std::vector<int>& padding,
+    const std::vector<int>& kernel_dilation,
+    const std::vector<int>& input_dilation,
+    int groups,
+    bool flip,
+    Stream s) {
+  if (!in.flags().row_contiguous) {
+    in = contiguous_copy_gpu(in, s);
+    encoder.add_temporary(in);
+  }
+  if (!wt.flags().row_contiguous) {
+    wt = contiguous_copy_gpu(wt, s);
+    encoder.add_temporary(wt);
+  }
+
+  if (groups == 1) {
+    gemm_conv(
+        encoder,
+        in,
+        wt,
+        out,
+        strides,
+        padding,
+        kernel_dilation,
+        input_dilation,
+        flip,
+        s);
+  } else {
+    gemm_grouped_conv(
+        encoder,
+        in,
+        wt,
+        out,
+        strides,
+        padding,
+        kernel_dilation,
+        input_dilation,
+        groups,
+        flip,
+        s);
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cuda/conv/gemm_conv.cu

@@ -0,0 +1,217 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/conv/conv.h"
+#include "mlx/backend/cuda/gemms/cublas_gemm.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/dtype_utils.h"
+
+#include <cooperative_groups.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+template <typename T, int NDIM>
+__global__ void naive_unfold_nd(
+    const T* in,
+    T* out,
+    int filter_size,
+    int out_pixels,
+    const __grid_constant__ ConvParams<NDIM> params) {
+  auto block = cg::this_thread_block();
+  auto tid = block.group_index();
+  auto lid = block.thread_index();
+
+  int index_batch = tid.z / out_pixels; // [0, N)
+  int index_out_spatial = tid.z % out_pixels; // [0, H_out * W_out)
+  int index_wt_spatial =
+      tid.x * block.dim_threads().x + lid.x; // [0, H_wt * W_wt)
+
+  if (index_wt_spatial >= filter_size / params.C) {
+    return;
+  }
+
+  in += tid.y; // [0, C)
+  out += tid.z * filter_size + index_wt_spatial * params.C + tid.y;
+
+  bool valid = index_batch < params.N;
+
+  // Get the coordinates in input.
+  int index_in[NDIM] = {};
+#pragma unroll
+  for (int i = NDIM - 1; i >= 0; --i) {
+    int index_out = index_out_spatial % params.out_spatial_dims[i];
+    int index_wt = index_wt_spatial % params.wt_spatial_dims[i];
+
+    if (params.flip) {
+      index_wt = params.wt_spatial_dims[i] - index_wt - 1;
+    }
+
+    int index = index_out * params.strides[i] - params.padding[i] +
+        index_wt * params.kernel_dilation[i];
+    int index_max =
+        1 + params.input_dilation[i] * (params.in_spatial_dims[i] - 1);
+
+    valid &= (index >= 0) && (index < index_max) &&
+        (index % params.input_dilation[i] == 0);
+
+    index_in[i] = index / params.input_dilation[i];
+
+    index_out_spatial /= params.out_spatial_dims[i];
+    index_wt_spatial /= params.wt_spatial_dims[i];
+  }
+
+  if (valid) {
+    int in_offset = index_batch * params.in_strides[0];
+#pragma unroll
+    for (int i = 0; i < NDIM; ++i) {
+      in_offset += index_in[i] * params.in_strides[i + 1];
+    }
+    *out = in[in_offset];
+  } else {
+    *out = T{0};
+  }
+}
+
+} // namespace cu
+
+template <int NDIM>
+array unfold_inputs_nd(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    int mat_M,
+    int mat_K,
+    int mat_N,
+    ConvParams<NDIM>& params) {
+  array unfolded({mat_M, mat_K}, in.dtype(), nullptr, {});
+  unfolded.set_data(allocator::malloc(unfolded.nbytes()));
+  encoder.add_temporary(unfolded);
+
+  int filter_size = params.C;
+#pragma unroll
+  for (int i = 0; i < NDIM; ++i) {
+    filter_size *= params.wt_spatial_dims[i];
+  }
+
+  int out_pixels = 1;
+#pragma unroll
+  for (int i = 0; i < NDIM; ++i) {
+    out_pixels *= params.out_spatial_dims[i];
+  }
+
+  int wt_spatial_size = mat_K / params.C;
+  dim3 block_dims;
+  block_dims.x = std::min(std::max(wt_spatial_size, 32), 1024);
+  dim3 num_blocks;
+  num_blocks.x = cuda::ceil_div(wt_spatial_size, block_dims.x);
+  num_blocks.y = params.C;
+  num_blocks.z = mat_M;
+
+  encoder.set_input_array(in);
+  encoder.set_output_array(unfolded);
+  dispatch_float_types(in.dtype(), "unfold", [&](auto type_tag) {
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    encoder.add_kernel_node(
+        cu::naive_unfold_nd<DataType, NDIM>,
+        num_blocks,
+        block_dims,
+        0,
+        in.data<DataType>(),
+        unfolded.data<DataType>(),
+        filter_size,
+        out_pixels,
+        params);
+  });
+
+  return unfolded;
+}
+
+template <int NDIM>
+void gemm_conv_nd(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    const array& wt,
+    array& out,
+    ConvParams<NDIM>& params,
+    Stream s) {
+  // Get gemm shapes.
+  int mat_M = out.size() / params.O; // N * H_out * W_out
+  int mat_K = wt.size() / params.O; // C * H_wt * W_wt
+  int mat_N = params.O; // O
+
+  // Unfold input to (N * H_out * W_out, C * H_wt * W_wt) for gemm.
+  array in_unfolded =
+      unfold_inputs_nd<NDIM>(encoder, in, mat_M, mat_K, mat_N, params);
+
+  // Reshape weight to (C * H_wt * W_wt, O) for gemm.
+  array wt_reshaped({mat_K, mat_N}, wt.dtype(), nullptr, {});
+  wt_reshaped.copy_shared_buffer(
+      wt,
+      {1, mat_K},
+      {false, false, /* col_contiguous */ true},
+      wt.data_size());
+
+  // Single batch.
+  Shape batch_shape{1};
+  Strides a_batch_strides{0};
+  Strides b_batch_strides{0};
+
+  // Run matmul.
+  CublasGemm gemm(
+      encoder.device(),
+      in.dtype(),
+      false, // a_transposed
+      mat_M, // a_rows
+      mat_K, // a_cols
+      mat_K, // lda
+      true, // b_transposed
+      mat_K, // b_rows
+      mat_N, // b_cols
+      mat_K, // ldb
+      batch_shape.back(),
+      a_batch_strides.back(),
+      b_batch_strides.back());
+  gemm.run(
+      encoder,
+      out,
+      in_unfolded,
+      wt_reshaped,
+      batch_shape,
+      a_batch_strides,
+      b_batch_strides);
+}
+
+void gemm_conv(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    const array& wt,
+    array& out,
+    const std::vector<int>& strides,
+    const std::vector<int>& padding,
+    const std::vector<int>& kernel_dilation,
+    const std::vector<int>& input_dilation,
+    bool flip,
+    Stream s) {
+  int conv_ndim = in.ndim() - 2;
+  if (conv_ndim < 1 || conv_ndim > 3) {
+    throw std::runtime_error(
+        fmt::format("[conv] Unsupported gemm_conv for {}D conv.", conv_ndim));
+  }
+  dispatch_1_2_3(conv_ndim, [&](auto ndim_constant) {
+    ConvParams<ndim_constant()> params(
+        in,
+        wt,
+        out,
+        strides,
+        padding,
+        kernel_dilation,
+        input_dilation,
+        1, // groups
+        flip);
+    gemm_conv_nd<ndim_constant()>(encoder, in, wt, out, params, s);
+  });
+}
+
+} // namespace mlx::core

mlx/backend/cuda/conv/gemm_grouped_conv.cu

@@ -0,0 +1,231 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/conv/conv.h"
+#include "mlx/backend/cuda/gemms/cublas_gemm.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/dtype_utils.h"
+
+#include <cooperative_groups.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+template <typename T, int NDIM>
+__global__ void naive_grouped_unfold_transpose_nd(
+    const T* in,
+    T* out,
+    int filter_size,
+    int out_pixels,
+    const __grid_constant__ ConvParams<NDIM> params) {
+  auto block = cg::this_thread_block();
+  auto tid = block.group_index();
+  auto lid = block.thread_index();
+
+  int index_batch = tid.z / out_pixels; // [0, N)
+  int index_out_spatial = tid.z % out_pixels; // [0, H_out * W_out)
+  int index_wt_spatial =
+      tid.x * block.dim_threads().x + lid.x; // [0, H_wt * W_wt)
+
+  if (index_wt_spatial >= filter_size / params.C) {
+    return;
+  }
+
+  in += tid.y; // [0, C)
+  out += tid.z * filter_size + tid.y * (filter_size / params.C);
+
+  bool valid = index_batch < params.N;
+
+  // Get the coordinates in input.
+  int index_in[NDIM] = {};
+  int wt_stride = 1;
+#pragma unroll
+  for (int i = NDIM - 1; i >= 0; --i) {
+    int index_out = index_out_spatial % params.out_spatial_dims[i];
+    int index_wt = index_wt_spatial % params.wt_spatial_dims[i];
+    out += index_wt * wt_stride;
+
+    if (params.flip) {
+      index_wt = params.wt_spatial_dims[i] - index_wt - 1;
+    }
+
+    int index = index_out * params.strides[i] - params.padding[i] +
+        index_wt * params.kernel_dilation[i];
+    int index_max =
+        1 + params.input_dilation[i] * (params.in_spatial_dims[i] - 1);
+
+    valid &= (index >= 0) && (index < index_max) &&
+        (index % params.input_dilation[i] == 0);
+
+    index_in[i] = index / params.input_dilation[i];
+
+    index_out_spatial /= params.out_spatial_dims[i];
+    index_wt_spatial /= params.wt_spatial_dims[i];
+    wt_stride *= params.wt_spatial_dims[i];
+  }
+
+  if (valid) {
+    int in_offset = index_batch * params.in_strides[0];
+#pragma unroll
+    for (int i = 0; i < NDIM; ++i) {
+      in_offset += index_in[i] * params.in_strides[i + 1];
+    }
+    *out = in[in_offset];
+  } else {
+    *out = T{0};
+  }
+}
+
+} // namespace cu
+
+template <int NDIM>
+array grouped_unfold_transpose_inputs_nd(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    int mat_M,
+    int mat_K,
+    int mat_N,
+    ConvParams<NDIM>& params) {
+  array unfolded({mat_M, mat_K * params.groups}, in.dtype(), nullptr, {});
+  unfolded.set_data(allocator::malloc(unfolded.nbytes()));
+  encoder.add_temporary(unfolded);
+
+  int filter_size = params.C;
+#pragma unroll
+  for (int i = 0; i < NDIM; ++i) {
+    filter_size *= params.wt_spatial_dims[i];
+  }
+
+  int out_pixels = 1;
+#pragma unroll
+  for (int i = 0; i < NDIM; ++i) {
+    out_pixels *= params.out_spatial_dims[i];
+  }
+
+  int wt_spatial_size = (mat_K * params.groups) / params.C;
+  dim3 block_dims;
+  block_dims.x = std::min(std::max(wt_spatial_size, 32), 1024);
+  dim3 num_blocks;
+  num_blocks.x = cuda::ceil_div(wt_spatial_size, block_dims.x);
+  num_blocks.y = params.C;
+  num_blocks.z = mat_M;
+
+  encoder.set_input_array(in);
+  encoder.set_output_array(unfolded);
+  dispatch_float_types(in.dtype(), "unfold", [&](auto type_tag) {
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    encoder.add_kernel_node(
+        cu::naive_grouped_unfold_transpose_nd<DataType, NDIM>,
+        num_blocks,
+        block_dims,
+        0,
+        in.data<DataType>(),
+        unfolded.data<DataType>(),
+        filter_size,
+        out_pixels,
+        params);
+  });
+
+  return unfolded;
+}
+
+template <int NDIM>
+void gemm_grouped_conv_nd(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    const array& wt,
+    array& out,
+    ConvParams<NDIM>& params,
+    Stream s) {
+  // Get gemm shapes.
+  int C_per_group = params.C / params.groups;
+  int O_per_group = params.O / params.groups;
+  int mat_M = out.size() / params.O; // N * H_out * W_out
+  int mat_K = wt.size() / params.O; // C_per_group * H_wt * W_wt
+  int mat_N = O_per_group; // O_per_group
+
+  // Unfold input to (N * H_out * W_out, C * H_wt * W_wt) for gemm.
+  array in_unfolded = grouped_unfold_transpose_inputs_nd<NDIM>(
+      encoder, in, mat_M, mat_K, mat_N, params);
+
+  // Reshape weight to (O, C_per_group, H_wt * W_wt) for gemm.
+  int wt_spatial_size = (wt.size() / wt.shape(0)) / wt.shape(-1);
+  array wt_view(
+      {params.O, C_per_group, wt_spatial_size}, wt.dtype(), nullptr, {});
+  wt_view.copy_shared_buffer(
+      wt, {wt.strides(0), 1, C_per_group}, wt.flags(), wt.size());
+  array wt_reshaped = contiguous_copy_gpu(wt_view, s);
+
+  // Batch with size of groups.
+  Shape batch_shape{params.groups};
+  Strides a_batch_strides{mat_K};
+  Strides b_batch_strides{mat_N * mat_K};
+
+  // Run matmul.
+  CublasGemm gemm(
+      encoder.device(),
+      in.dtype(),
+      false, // a_transposed
+      mat_M, // a_rows
+      mat_K, // a_cols
+      mat_K * params.groups, // lda
+      true, // b_transposed
+      mat_K, // b_rows
+      mat_N, // b_cols
+      mat_K, // ldb
+      batch_shape.back(),
+      a_batch_strides.back(),
+      b_batch_strides.back());
+  gemm.set_out(
+      out.dtype(),
+      false, // out_transposed
+      mat_M, // out_rows
+      mat_N, // out_cols
+      mat_N * params.groups, // out_ld
+      params.groups, // batch_count
+      mat_N); // batch_stride
+  gemm.run(
+      encoder,
+      out,
+      in_unfolded,
+      wt_reshaped,
+      batch_shape,
+      a_batch_strides,
+      b_batch_strides);
+}
+
+void gemm_grouped_conv(
+    cu::CommandEncoder& encoder,
+    const array& in,
+    const array& wt,
+    array& out,
+    const std::vector<int>& strides,
+    const std::vector<int>& padding,
+    const std::vector<int>& kernel_dilation,
+    const std::vector<int>& input_dilation,
+    int groups,
+    bool flip,
+    Stream s) {
+  int conv_ndim = in.ndim() - 2;
+  if (conv_ndim < 1 || conv_ndim > 3) {
+    throw std::runtime_error(
+        fmt::format("[conv] Unsupported gemm_conv for {}D conv.", conv_ndim));
+  }
+  dispatch_1_2_3(conv_ndim, [&](auto ndim_constant) {
+    ConvParams<ndim_constant()> params(
+        in,
+        wt,
+        out,
+        strides,
+        padding,
+        kernel_dilation,
+        input_dilation,
+        groups,
+        flip);
+    gemm_grouped_conv_nd<ndim_constant()>(encoder, in, wt, out, params, s);
+  });
+}
+
+} // namespace mlx::core

mlx/backend/cuda/copy/copy_contiguous.cu

@@ -22,7 +22,7 @@ __global__ void copy_s(const In* in, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = cast_to<Out>(in[0]);
+      out_vec[i] = cast_to<Out>(in[0]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -43,7 +43,7 @@ __global__ void copy_v(const In* in, Out* out, IdxT size) {
     AlignedVector<Out, N_READS> out_vec;
 #pragma unroll
     for (int i = 0; i < N_READS; ++i) {
-      out_vec.val[i] = cast_to<Out>(in_vec.val[i]);
+      out_vec[i] = cast_to<Out>(in_vec[i]);
     }
 
     store_vector<N_READS>(out, index, out_vec);
@@ -65,23 +65,18 @@ void copy_contiguous(
         using InType = cuda_type_t<MLX_GET_TYPE(in_type_tag)>;
         using OutType = cuda_type_t<MLX_GET_TYPE(out_type_tag)>;
         using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
-        // TODO: Choose optimized value based on type size.
-        constexpr int N_READS = 4;
+        constexpr int N_READS = 16 / sizeof(InType);
         auto kernel = cu::copy_s<InType, OutType, IdxT, N_READS>;
         if (ctype == CopyType::Vector) {
           kernel = cu::copy_v<InType, OutType, IdxT, N_READS>;
         }
         auto [num_blocks, block_dims] = get_launch_args(
-            kernel,
-            out.data_size(),
-            out.shape(),
-            out.strides(),
-            large(),
-            N_READS);
+            out.data_size(), out.shape(), out.strides(), large(), N_READS);
         encoder.add_kernel_node(
             kernel,
             num_blocks,
             block_dims,
+            0,
             in.data<InType>() + in_offset,
             out.data<OutType>() + out_offset,
             out.data_size());

mlx/backend/cuda/copy/copy_general.cu

@@ -10,37 +10,80 @@ namespace cu {
 
 namespace cg = cooperative_groups;
 
-template <typename In, typename Out, typename IdxT, int NDIM>
+template <typename In, typename Out, typename IdxT, int NDIM, int N_READS>
 __global__ void copy_gg_nd(
     const In* in,
     Out* out,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in,
     const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_out) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc_nd<NDIM>(
-        index, shape.data(), strides_in.data(), strides_out.data());
-    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[NDIM - 1];
+  auto in_stride_x = strides_in[NDIM - 1];
+  auto out_stride_x = strides_out[NDIM - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [idx_in, idx_out] = elem_to_loc_nd<NDIM>(
+      index_rest * shape_x,
+      shape.data(),
+      strides_in.data(),
+      strides_out.data());
+
+  auto in_vec =
+      load_vector<N_READS>(in + idx_in, index_x, shape_x, in_stride_x, In(0));
+  AlignedVector<Out, N_READS> out_vec;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    out_vec[i] = CastOp<In, Out>{}(in_vec[i]);
+  }
+  store_vector(out + idx_out, index_x, out_vec, shape_x, out_stride_x);
 }
 
-template <typename In, typename Out, typename IdxT>
+template <typename In, typename Out, typename IdxT, int N_READS>
 __global__ void copy_gg(
     const In* in,
     Out* out,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ Shape shape,
     const __grid_constant__ Strides strides_in,
     const __grid_constant__ Strides strides_out,
     int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc_4d(
-        index, shape.data(), strides_in.data(), strides_out.data(), ndim);
-    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[ndim - 1];
+  auto in_stride_x = strides_in[ndim - 1];
+  auto out_stride_x = strides_out[ndim - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto [idx_in, idx_out] = elem_to_loc(
+      index_rest * shape_x,
+      shape.data(),
+      strides_in.data(),
+      strides_out.data(),
+      ndim);
+
+  auto in_vec =
+      load_vector<N_READS>(in + idx_in, index_x, shape_x, in_stride_x, In(0));
+  AlignedVector<Out, N_READS> out_vec;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    out_vec[i] = CastOp<In, Out>{}(in_vec[i]);
+  }
+  store_vector(out + idx_out, index_x, out_vec, shape_x, out_stride_x);
 }
 
 } // namespace cu
@@ -69,34 +112,52 @@ void copy_general(
             size_t data_size = 1;
             for (auto& s : shape)
               data_size *= s;
+
+            int work_per_thread = 1;
+            auto dim0 = ndim > 0 ? shape.back() : 1;
+            auto rest = data_size / dim0;
+            if (dim0 >= 4) {
+              work_per_thread = 4;
+            }
+
+            dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
+            auto block_dims = get_block_dims(dim0, rest, 1);
+            uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
+            uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
+
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto ndim_constant) {
                 auto kernel =
-                    cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant()>;
-                auto [num_blocks, block_dims] = get_launch_args(
-                    kernel, data_size, shape, out.strides(), large());
+                    cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant(), 1>;
+                if (work_per_thread == 4) {
+                  kernel =
+                      cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant(), 4>;
+                }
                 encoder.add_kernel_node(
                     kernel,
-                    num_blocks,
+                    {num_blocks_x, num_blocks_y},
                     block_dims,
+                    0,
                     in_ptr,
                     out_ptr,
-                    data_size,
+                    rest,
                     const_param<ndim_constant()>(shape),
                     const_param<ndim_constant()>(strides_in),
                     const_param<ndim_constant()>(strides_out));
               });
             } else { // ndim >= 4
-              auto kernel = cu::copy_gg<InType, OutType, IdxT>;
-              auto [num_blocks, block_dims] = get_launch_args(
-                  kernel, data_size, shape, out.strides(), large());
+              auto kernel = cu::copy_gg<InType, OutType, IdxT, 1>;
+              if (work_per_thread == 4) {
+                kernel = cu::copy_gg<InType, OutType, IdxT, 4>;
+              }
               encoder.add_kernel_node(
                   kernel,
-                  num_blocks,
+                  {num_blocks_x, num_blocks_y},
                   block_dims,
+                  0,
                   in_ptr,
                   out_ptr,
-                  data_size,
+                  rest,
                   const_param(shape),
                   const_param(strides_in),
                   const_param(strides_out),

mlx/backend/cuda/copy/copy_general_dynamic.cu

@@ -41,7 +41,7 @@ __global__ void copy_gg_dynamic(
     const int64_t* offset_out) {
   IdxT index = cg::this_grid().thread_rank();
   if (index < size) {
-    auto [idx_in, idx_out] = elem_to_loc_4d(
+    auto [idx_in, idx_out] = elem_to_loc(
         index, shape.data(), strides_in.data(), strides_out.data(), ndim);
     out[idx_out + *offset_out] = CastOp<In, Out>{}(in[idx_in + *offset_in]);
   }
@@ -74,14 +74,16 @@ void copy_general_dynamic(
             int ndim = shape.size();
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto dims_constant) {
-                auto kernel = cu::
-                    copy_gg_dynamic_nd<InType, OutType, IdxT, dims_constant()>;
-                auto [num_blocks, block_dims] =
-                    get_launch_args(kernel, out, large());
+                auto [num_blocks, block_dims] = get_launch_args(out, large());
                 encoder.add_kernel_node(
-                    kernel,
+                    cu::copy_gg_dynamic_nd<
+                        InType,
+                        OutType,
+                        IdxT,
+                        dims_constant()>,
                     num_blocks,
                     block_dims,
+                    0,
                     in_ptr,
                     out_ptr,
                     out.size(),
@@ -92,13 +94,12 @@ void copy_general_dynamic(
                     dynamic_offset_out.data<int64_t>());
               });
             } else { // ndim >= 4
-              auto kernel = cu::copy_gg_dynamic<InType, OutType, IdxT>;
-              auto [num_blocks, block_dims] =
-                  get_launch_args(kernel, out, large());
+              auto [num_blocks, block_dims] = get_launch_args(out, large());
               encoder.add_kernel_node(
-                  kernel,
+                  cu::copy_gg_dynamic<InType, OutType, IdxT>,
                   num_blocks,
                   block_dims,
+                  0,
                   in_ptr,
                   out_ptr,
                   out.size(),

mlx/backend/cuda/copy/copy_general_input.cu

@@ -10,33 +10,67 @@ namespace cu {
 
 namespace cg = cooperative_groups;
 
-template <typename In, typename Out, typename IdxT, int NDIM>
+template <typename In, typename Out, typename IdxT, int NDIM, int N_READS>
 __global__ void copy_g_nd(
     const In* in,
     Out* out,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides_in) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    IdxT idx_in = elem_to_loc_nd<NDIM>(index, shape.data(), strides_in.data());
-    out[index] = CastOp<In, Out>{}(in[idx_in]);
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> strides) {
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[NDIM - 1];
+  auto stride_x = strides[NDIM - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto idx =
+      elem_to_loc_nd<NDIM>(index_rest * shape_x, shape.data(), strides.data());
+  auto in_vec =
+      load_vector<N_READS>(in + idx, index_x, shape_x, stride_x, In(0));
+  AlignedVector<Out, N_READS> out_vec;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    out_vec[i] = CastOp<In, Out>{}(in_vec[i]);
+  }
+  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
-template <typename In, typename Out, typename IdxT>
+template <typename In, typename Out, typename IdxT, int N_READS>
 __global__ void copy_g(
     const In* in,
     Out* out,
-    IdxT size,
+    IdxT size_rest,
     const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides_in,
+    const __grid_constant__ Strides strides,
     int ndim) {
-  IdxT index = cg::this_grid().thread_rank();
-  if (index < size) {
-    IdxT idx_in = elem_to_loc_4d(index, shape.data(), strides_in.data(), ndim);
-    out[index] = CastOp<In, Out>{}(in[idx_in]);
+  auto block = cg::this_thread_block();
+  auto grid = cg::this_grid();
+  IdxT index_rest =
+      grid.block_index().y * block.dim_threads().y + block.thread_index().y;
+  if (index_rest >= size_rest) {
+    return;
   }
+
+  auto shape_x = shape[ndim - 1];
+  auto stride_x = strides[ndim - 1];
+  IdxT index_x =
+      grid.block_index().x * block.dim_threads().x + block.thread_index().x;
+  auto idx =
+      elem_to_loc(index_rest * shape_x, shape.data(), strides.data(), ndim);
+  auto in_vec =
+      load_vector<N_READS>(in + idx, index_x, shape_x, stride_x, In(0));
+  AlignedVector<Out, N_READS> out_vec;
+#pragma unroll
+  for (int i = 0; i < N_READS; ++i) {
+    out_vec[i] = CastOp<In, Out>{}(in_vec[i]);
+  }
+  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }
 
 } // namespace cu
@@ -61,33 +95,49 @@ void copy_general_input(
             const InType* in_ptr = in.data<InType>() + offset_in;
             OutType* out_ptr = out.data<OutType>() + offset_out;
             int ndim = shape.size();
+            int work_per_thread = 1;
+            auto dim0 = ndim > 0 ? shape.back() : 1;
+            auto rest = out.size() / dim0;
+            if (dim0 >= 4) {
+              work_per_thread = 4;
+            }
+            dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
+            auto block_dims = get_block_dims(dim0, rest, 1);
+            uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
+            uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
+
             if (ndim <= 3) {
               dispatch_1_2_3(ndim, [&](auto dims_constant) {
                 auto kernel =
-                    cu::copy_g_nd<InType, OutType, IdxT, dims_constant()>;
-                auto [num_blocks, block_dims] =
-                    get_launch_args(kernel, out, large());
+                    cu::copy_g_nd<InType, OutType, IdxT, dims_constant(), 1>;
+                if (work_per_thread == 4) {
+                  kernel =
+                      cu::copy_g_nd<InType, OutType, IdxT, dims_constant(), 4>;
+                }
                 encoder.add_kernel_node(
                     kernel,
-                    num_blocks,
+                    {num_blocks_x, num_blocks_y},
                     block_dims,
+                    0,
                     in_ptr,
                     out_ptr,
-                    out.size(),
+                    rest,
                     const_param<dims_constant()>(shape),
                     const_param<dims_constant()>(strides_in));
               });
             } else { // ndim >= 4
-              auto kernel = cu::copy_g<InType, OutType, IdxT>;
-              auto [num_blocks, block_dims] =
-                  get_launch_args(kernel, out, large());
+              auto kernel = cu::copy_g<InType, OutType, IdxT, 1>;
+              if (work_per_thread == 4) {
+                kernel = cu::copy_g<InType, OutType, IdxT, 4>;
+              }
               encoder.add_kernel_node(
                   kernel,
-                  num_blocks,
+                  {num_blocks_x, num_blocks_y},
                   block_dims,
+                  0,
                   in_ptr,
                   out_ptr,
-                  out.size(),
+                  rest,
                   const_param(shape),
                   const_param(strides_in),
                   ndim);

mlx/backend/cuda/cudnn_utils.cpp

@@ -0,0 +1,272 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/cudnn_utils.h"
+#include "mlx/backend/cuda/device.h"
+
+namespace mlx::core {
+
+namespace {
+
+// Create a cudnn tensor descriptor.
+template <typename Vec>
+inline cudnn_frontend::Tensor build_cudnn_tensor(
+    int64_t id,
+    const array& x,
+    const Vec& shape,
+    const Vec& strides) {
+  return cudnn_frontend::TensorBuilder()
+      .setDim(shape.size(), shape.data())
+      .setStrides(strides.size(), strides.data())
+      .setId(id)
+      .setAlignment(get_alignment(x))
+      .setDataType(dtype_to_cudnn_type(x.dtype()))
+      .build();
+}
+
+// In MLX a singleton dim (shape[dim] == 1) can have any stride, but in cuDNN
+// whether a tensor is contiguous is determined with:
+// shape[dim] == shape[dim + 1] * strides[dim + 1]
+// So a contiguous array with singleton dims in MLX may be mistakenly treated
+// as strided in cuDNN, and we work around it by normalizing the strides.
+Strides normalized_strides(const array& x) {
+  if (!x.flags().row_contiguous || x.ndim() < 2) {
+    return x.strides();
+  }
+  Strides strides = x.strides();
+  for (int i = x.ndim() - 2; i >= 0; --i) {
+    if (x.shape(i) == 1) {
+      strides[i] = x.shape(i + 1) * strides[i + 1];
+    }
+  }
+  return strides;
+}
+
+// Return the shape and strides after transposing from NHWC to NCHW.
+auto nhwc_to_nchw(SmallVector<int64_t> shape, SmallVector<int64_t> strides) {
+  assert(shape.size() >= 3);
+  shape.insert(shape.begin() + 1, shape.back());
+  shape.erase(shape.end() - 1);
+  strides.insert(strides.begin() + 1, strides.back());
+  strides.erase(strides.end() - 1);
+  return std::make_tuple(std::move(shape), std::move(strides));
+}
+
+inline auto nhwc_to_nchw(const array& x) {
+  return nhwc_to_nchw(
+      convert_vector<int64_t>(x.shape()), normalized_strides(x));
+}
+
+// Return available engines for a |op_graph|.
+cudnn_frontend::EngineConfigList get_cudnn_engine_configs(
+    cudnnBackendDescriptorType_t backend_type,
+    Dtype dtype,
+    cudnn_frontend::OperationGraph& op_graph,
+    bool use_fallback = true) {
+  SmallVector<cudnn_frontend::GeneratorSource, 2> sources;
+  sources.push_back([](auto& op_graph) {
+    auto heuristics = cudnn_frontend::EngineHeuristicsBuilder()
+                          .setOperationGraph(op_graph)
+                          .setHeurMode(CUDNN_HEUR_MODE_A)
+                          .build();
+    return heuristics.getEngineConfig(heuristics.getEngineConfigCount());
+  });
+  if (use_fallback) {
+    sources.push_back([&backend_type](auto& op_graph) {
+      auto fallback = cudnn_frontend::EngineFallbackListBuilder()
+                          .setOperationGraph(op_graph)
+                          .setOperation(backend_type)
+                          .build();
+      return fallback.getFallbackList();
+    });
+  }
+
+  auto configs =
+      cudnn_frontend::EngineConfigGenerator(sources.size(), sources.data())
+          .generate_engine_config(op_graph);
+
+  cudnn_frontend::EngineConfigList filtered_configs;
+  cudnn_frontend::filter(configs, filtered_configs, [dtype](auto c) {
+    if (cudnn_frontend::hasNumericalNote<
+            CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS>(c)) {
+      return true;
+    }
+    if (cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_TENSOR_CORE>(c) &&
+        dtype == float32 && !env::enable_tf32()) {
+      return true;
+    }
+    return false;
+  });
+  return filtered_configs;
+}
+
+// Take |engine_configs| and |op_graph| and find a working execution plans
+// from them.
+std::optional<cudnn_frontend::ExecutionPlan>
+find_cudnn_plan_from_engine_configs(
+    cudnnHandle_t handle,
+    const cudnn_frontend::EngineConfigList& engine_configs,
+    const cudnn_frontend::OperationGraph& op_graph) {
+  auto op_graph_tag = op_graph.getTag();
+  for (const auto& config : engine_configs) {
+    try {
+      return cudnn_frontend::ExecutionPlanBuilder()
+          .setHandle(handle)
+          .setEngineConfig(config, op_graph_tag)
+          .build();
+    } catch (cudnn_frontend::cudnnException& error) {
+      if (error.getCudnnStatus() != CUDNN_STATUS_NOT_SUPPORTED) {
+        throw;
+      }
+    }
+  }
+  return std::nullopt;
+}
+
+// Prepare workspace and args to execute plan.
+template <typename F>
+bool prepare_cudnn_plan(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::ExecutionPlan& plan,
+    int num_args,
+    const int64_t* uids,
+    void** data_ptrs,
+    F&& execute) {
+  int workspace_size = plan.getWorkspaceSize();
+  array workspace(
+      workspace_size > 0 ? allocator::malloc(workspace_size)
+                         : allocator::Buffer(nullptr),
+      {workspace_size},
+      uint8);
+
+  auto args = cudnn_frontend::VariantPackBuilder()
+                  .setWorkspacePointer(workspace.data<void>())
+                  .setDataPointers(num_args, data_ptrs)
+                  .setUids(num_args, uids)
+                  .build();
+
+  auto handle = encoder.device().cudnn_handle();
+  cudnnSetStream(handle, encoder.stream());
+
+  if (!execute(handle, plan.get_raw_desc(), args.get_raw_desc())) {
+    return false;
+  }
+
+  encoder.add_temporary(workspace);
+  return true;
+}
+
+} // namespace
+
+cudnn_frontend::Tensor build_cudnn_tensor(int64_t id, const array& x) {
+  auto shape = convert_vector<int64_t>(x.shape());
+  return build_cudnn_tensor(id, x, shape, normalized_strides(x));
+}
+
+cudnn_frontend::Tensor build_cudnn_tensor_nchw(int64_t id, const array& x) {
+  auto [shape, strides] = nhwc_to_nchw(x);
+  return build_cudnn_tensor(id, x, shape, strides);
+}
+
+cudnn_frontend::Tensor build_cudnn_tensor_4d_nchw(int64_t id, const array& x) {
+  if (x.ndim() == 0) {
+    SmallVector<int64_t, 4> scalar_dims = {1, 1, 1, 1};
+    return build_cudnn_tensor(id, x, scalar_dims, scalar_dims);
+  }
+  if (x.ndim() == 1) {
+    int64_t s = x.shape(0);
+    SmallVector<int64_t, 4> shape = {1, x.shape(0), 1, 1};
+    SmallVector<int64_t, 4> strides = {s, 1, s, s};
+    return build_cudnn_tensor(id, x, shape, strides);
+  }
+  if (x.ndim() == 2) {
+    int64_t s =
+        x.flags().row_contiguous ? x.shape(1) * x.strides(1) : x.strides(0);
+    SmallVector<int64_t, 4> shape = {x.shape(0), x.shape(1), 1, 1};
+    SmallVector<int64_t, 4> strides = {s, x.strides(1), s, s};
+    return build_cudnn_tensor(id, x, shape, strides);
+  }
+  if (x.ndim() == 3 || x.ndim() == 4) {
+    return build_cudnn_tensor_nchw(id, x);
+  }
+  throw std::runtime_error(
+      fmt::format("Unsupported array with {} dims.", x.ndim()));
+}
+
+cudnn_frontend::Tensor build_cudnn_scalar_4d(int64_t id, Dtype dtype) {
+  SmallVector<int64_t, 4> scalar_dims = {1, 1, 1, 1};
+  return cudnn_frontend::TensorBuilder()
+      .setDim(scalar_dims.size(), scalar_dims.data())
+      .setStrides(scalar_dims.size(), scalar_dims.data())
+      .setId(id)
+      .setAlignment(16)
+      .setDataType(dtype_to_cudnn_type(dtype))
+      .setByValue(true)
+      .build();
+}
+
+std::optional<cudnn_frontend::ExecutionPlan> find_cudnn_plan_from_op_graph(
+    cudnnHandle_t handle,
+    cudnnBackendDescriptorType_t backend_type,
+    Dtype dtype,
+    cudnn_frontend::OperationGraph& op_graph) {
+  auto engine_configs = get_cudnn_engine_configs(backend_type, dtype, op_graph);
+  return find_cudnn_plan_from_engine_configs(handle, engine_configs, op_graph);
+}
+
+bool encode_cudnn_plan_with_capturing(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::ExecutionPlan& plan,
+    int num_args,
+    const int64_t* uids,
+    void** data_ptrs) {
+  return prepare_cudnn_plan(
+      encoder,
+      plan,
+      num_args,
+      uids,
+      data_ptrs,
+      [&](auto handle, auto plan, auto args) {
+        auto capture = encoder.capture_context();
+        if (cudnnBackendExecute(handle, plan, args) != CUDNN_STATUS_SUCCESS) {
+          // Discard the captured graph when failed.
+          capture.discard = true;
+          return false;
+        }
+        return true;
+      });
+}
+
+#if CUDNN_VERSION >= 90500
+bool encode_cudnn_plan_with_graph_api(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::ExecutionPlan& plan,
+    CudaGraph& graph,
+    int num_args,
+    const int64_t* uids,
+    void** data_ptrs) {
+  return prepare_cudnn_plan(
+      encoder,
+      plan,
+      num_args,
+      uids,
+      data_ptrs,
+      [&](auto handle, auto plan, auto args) {
+        if (!graph) {
+          graph = CudaGraph(encoder.device());
+          if (cudnnBackendPopulateCudaGraph(handle, plan, args, graph) !=
+              CUDNN_STATUS_SUCCESS) {
+            return false;
+          }
+        } else {
+          if (cudnnBackendUpdateCudaGraph(handle, plan, args, graph) !=
+              CUDNN_STATUS_SUCCESS) {
+            return false;
+          }
+        }
+        encoder.add_graph_node(graph);
+        return true;
+      });
+}
+#endif
+
+} // namespace mlx::core

mlx/backend/cuda/cudnn_utils.h

@@ -0,0 +1,164 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/array.h"
+#include "mlx/backend/cuda/device/config.h"
+#include "mlx/backend/cuda/utils.h"
+#include "mlx/dtype_utils.h"
+
+#include <cudnn_frontend.h>
+#include <cudnn_frontend_find_plan.h>
+#include <fmt/format.h>
+
+#include <algorithm>
+#include <array>
+
+namespace mlx::core {
+
+namespace cu {
+class CommandEncoder;
+}
+
+// Return pointer alignment of |x|'s data.
+inline uint8_t get_alignment(const array& x) {
+  uint8_t alignment = 1;
+  uintptr_t address = reinterpret_cast<uintptr_t>(x.data<void>());
+  for (; alignment < 32; alignment *= 2) {
+    if (address % (alignment * 2)) {
+      return alignment;
+    }
+  }
+  return alignment;
+}
+
+// Convert the type of elements in |vec| to |T|.
+template <typename T, typename Vec>
+inline SmallVector<T> convert_vector(const Vec& vec) {
+  return SmallVector<T>(vec.begin(), vec.end());
+}
+
+// Return an array that can be used as map key for |vec| with size <= MAX_NDIM.
+//
+// There are 2 differences from the const_param util from kernel_utils.cuh:
+// 1. The rest of array is filled with 0.
+// 2. This util can be used in .cpp files.
+template <typename T, template <typename U> class Vec>
+inline std::array<T, MAX_NDIM> vector_key(const Vec<T>& vec) {
+  if (vec.size() > MAX_NDIM) {
+    throw std::runtime_error(
+        fmt::format("ndim can not be larger than {}.", MAX_NDIM));
+  }
+  std::array<T, MAX_NDIM> result = {};
+  std::copy_n(vec.begin(), vec.size(), result.begin());
+  return result;
+}
+
+// Helpers used by get_data_ptrs to get pointers.
+inline void* get_data_ptr(const array& arr) {
+  return const_cast<void*>(arr.data<void>());
+}
+
+template <typename T, typename = std::enable_if_t<std::is_scalar_v<T>>>
+inline void* get_data_ptr(T& scalar) {
+  return &scalar;
+}
+
+// Return an array filled with data pointers of args.
+template <typename... Args>
+inline std::array<void*, sizeof...(Args)> get_data_ptrs(Args&... args) {
+  return {get_data_ptr(args)...};
+}
+
+// Map dtype to cudnn data type.
+inline cudnnDataType_t dtype_to_cudnn_type(Dtype dtype) {
+  switch (dtype) {
+    case int8:
+      return CUDNN_DATA_INT8;
+    case int32:
+      return CUDNN_DATA_INT32;
+    case uint8:
+      return CUDNN_DATA_UINT8;
+    case float16:
+      return CUDNN_DATA_HALF;
+    case bfloat16:
+      return CUDNN_DATA_BFLOAT16;
+    case float32:
+      return CUDNN_DATA_FLOAT;
+    case float64:
+      return CUDNN_DATA_DOUBLE;
+    default:
+      throw std::runtime_error(fmt::format(
+          "Unsupported dtype in Convolution: {}.", dtype_to_string(dtype)));
+  }
+}
+
+// Create a tensor descriptor from |x|.
+cudnn_frontend::Tensor build_cudnn_tensor(int64_t id, const array& x);
+
+// Create a tensor descriptor from |x|, and transpose from NHWC to NCHW.
+cudnn_frontend::Tensor build_cudnn_tensor_nchw(int64_t id, const array& x);
+
+// Create a tensor descriptor from |x|, make sure it is 4D, and transpose it
+// from NHWC to NCHW.
+cudnn_frontend::Tensor build_cudnn_tensor_4d_nchw(int64_t id, const array& x);
+
+// Create a 4D scalar tensor descriptor, which is passed by value.
+cudnn_frontend::Tensor build_cudnn_scalar_4d(int64_t id, Dtype dtype);
+
+// Find a working plan for |op_graph|.
+std::optional<cudnn_frontend::ExecutionPlan> find_cudnn_plan_from_op_graph(
+    cudnnHandle_t handle,
+    cudnnBackendDescriptorType_t backend_type,
+    Dtype dtype,
+    cudnn_frontend::OperationGraph& op_graph);
+
+// Encode the plan to command buffer by capturing.
+bool encode_cudnn_plan_with_capturing(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::ExecutionPlan& plan,
+    int num_args,
+    const int64_t* uids,
+    void** data_ptrs);
+
+#if CUDNN_VERSION >= 90500
+// Encode the plan to command buffer by using native graph api of cudnn. If the
+// |graph| is empty it will be populated, otherwise it will be updated.
+bool encode_cudnn_plan_with_graph_api(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::ExecutionPlan& plan,
+    CudaGraph& graph,
+    int num_args,
+    const int64_t* uids,
+    void** data_ptrs);
+#endif
+
+// Helpers to make calls like encode_cudnn_plan(..., {'x', 'y', 'z'}, x, y, z).
+template <typename... Args>
+bool encode_cudnn_plan(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::ExecutionPlan& plan,
+    std::initializer_list<int64_t> uids,
+    Args&... args) {
+  assert(uids.size() == sizeof...(args));
+  auto data_ptrs = get_data_ptrs(args...);
+  return encode_cudnn_plan_with_capturing(
+      encoder, plan, uids.size(), uids.begin(), data_ptrs.data());
+}
+
+#if CUDNN_VERSION >= 90500
+template <typename... Args>
+bool encode_cudnn_plan(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::ExecutionPlan& plan,
+    CudaGraph& graph,
+    std::initializer_list<int64_t> uids,
+    Args&... args) {
+  assert(uids.size() == sizeof...(args));
+  auto data_ptrs = get_data_ptrs(args...);
+  return encode_cudnn_plan_with_graph_api(
+      encoder, plan, graph, uids.size(), uids.begin(), data_ptrs.data());
+}
+#endif
+
+} // namespace mlx::core

mlx/backend/cuda/custom_kernel.cpp

@@ -0,0 +1,379 @@
+// Copyright © 2025 Apple Inc.
+
+#include <iostream>
+
+#include "mlx/backend/common/compiled.h"
+#include "mlx/backend/cuda/jit_module.h"
+#include "mlx/backend/cuda/utils.h"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/fast.h"
+#include "mlx/fast_primitives.h"
+
+#include <fmt/format.h>
+#include <nvtx3/nvtx3.hpp>
+
+namespace mlx::core::fast {
+
+namespace {
+
+constexpr const char* default_header = R"(
+#include "mlx/backend/cuda/device/utils.cuh"
+
+#include <cooperative_groups.h>
+
+#define inf cuda::std::numeric_limits<float>::infinity()
+
+)";
+
+std::string template_arguments_hash(
+    const std::vector<std::pair<std::string, TemplateArg>>& template_args) {
+  if (template_args.empty()) {
+    return "";
+  }
+
+  std::string hash;
+  hash.reserve(512);
+
+  for (const auto& [name, arg] : template_args) {
+    if (std::holds_alternative<int>(arg)) {
+      hash += fmt::format("_{}", std::get<int>(arg));
+    } else if (std::holds_alternative<bool>(arg)) {
+      hash += (std::get<bool>(arg)) ? "_t" : "_f";
+    } else if (std::holds_alternative<Dtype>(arg)) {
+      hash += "_";
+      hash += get_type_string(std::get<Dtype>(arg));
+    }
+  }
+
+  return hash;
+}
+
+std::string build_kernel(
+    const std::string& func_name,
+    const std::string& header,
+    const std::string& source,
+    const std::vector<std::string>& input_names,
+    const std::vector<array>& inputs,
+    const std::vector<std::string>& output_names,
+    const std::vector<Dtype>& output_dtypes,
+    const std::vector<std::pair<std::string, TemplateArg>>& template_args,
+    const std::vector<CustomKernelShapeInfo>& shape_infos) {
+  std::string kernel_source;
+  kernel_source.reserve(header.size() + source.size() + 8192);
+  kernel_source += default_header;
+  kernel_source += header;
+  kernel_source +=
+      "namespace mlx::core::cu {\n\n"
+      "namespace cg = cooperative_groups;\n\n";
+
+  kernel_source += "__global__ void ";
+  kernel_source += func_name;
+  kernel_source += "(\n";
+
+  // Add inputs
+  for (int i = 0; i < inputs.size(); ++i) {
+    const auto& name = input_names[i];
+    const auto& arr = inputs[i];
+    kernel_source += "    const ";
+    kernel_source += dtype_to_cuda_type(arr.dtype());
+    kernel_source += "* ";
+    kernel_source += name;
+    kernel_source += ",\n";
+    // Add input shape, strides and ndim if present in the source
+    if (arr.ndim() > 0) {
+      if (shape_infos[i].shape) {
+        kernel_source += "    const __grid_constant__ Shape ";
+        kernel_source += name;
+        kernel_source += "_shape,\n";
+      }
+      if (shape_infos[i].strides) {
+        kernel_source += "    const __grid_constant__ Strides ";
+        kernel_source += name;
+        kernel_source += "_strides,\n";
+      }
+      if (shape_infos[i].ndim) {
+        kernel_source += "    const __grid_constant__ int ";
+        kernel_source += name;
+        kernel_source += "_ndim,\n";
+      }
+    }
+  }
+
+  // Add outputs
+  for (int i = 0; i < output_names.size(); ++i) {
+    const auto& name = output_names[i];
+    const auto& dtype = output_dtypes[i];
+    kernel_source += "    ";
+    kernel_source += dtype_to_cuda_type(dtype);
+    kernel_source += "* ";
+    kernel_source += name;
+    if (i < output_names.size() - 1) {
+      kernel_source += ",\n";
+    } else {
+      kernel_source += ") {\n";
+    }
+  }
+
+  // Set compile time constants
+  if (!template_args.empty()) {
+    for (const auto& [name, arg] : template_args) {
+      if (std::holds_alternative<int>(arg)) {
+        kernel_source +=
+            fmt::format("  constexpr int {} = {};\n", name, std::get<int>(arg));
+      } else if (std::holds_alternative<bool>(arg)) {
+        kernel_source += fmt::format(
+            "  constexpr bool {} = {};\n", name, std::get<bool>(arg));
+      } else {
+        kernel_source += fmt::format(
+            "  using {} = {};\n",
+            name,
+            dtype_to_cuda_type(std::get<Dtype>(arg)));
+      }
+    }
+    kernel_source += "\n";
+  }
+
+  kernel_source += source;
+  kernel_source += "\n}\n\n} // namespace mlx::core::cu\n";
+
+  return kernel_source;
+}
+
+} // namespace
+
+CustomKernelFunction cuda_kernel(
+    const std::string& name,
+    const std::vector<std::string>& input_names,
+    const std::vector<std::string>& output_names,
+    const std::string& source,
+    const std::string& header,
+    bool ensure_row_contiguous,
+    int shared_memory) {
+  if (output_names.empty()) {
+    throw std::invalid_argument(
+        "[custom_kernel] Must specify at least one output.");
+  }
+
+  std::vector<CustomKernelShapeInfo> shape_infos;
+  for (auto& n : input_names) {
+    CustomKernelShapeInfo shape_info;
+    shape_info.shape = source.find(n + "_shape") != std::string::npos;
+    shape_info.strides = source.find(n + "_strides") != std::string::npos;
+    shape_info.ndim = source.find(n + "_ndim") != std::string::npos;
+    shape_infos.push_back(shape_info);
+  }
+
+  return [=, shape_infos = std::move(shape_infos)](
+             const std::vector<array>& inputs,
+             const std::vector<Shape>& output_shapes,
+             const std::vector<Dtype>& output_dtypes,
+             std::tuple<int, int, int> grid,
+             std::tuple<int, int, int> threadgroup,
+             const std::vector<std::pair<std::string, TemplateArg>>&
+                 template_args = {},
+             std::optional<float> init_value = std::nullopt,
+             bool verbose = false,
+             StreamOrDevice s_ = {}) {
+    if (inputs.size() != input_names.size()) {
+      std::ostringstream msg;
+      msg << "[custom_kernel] Expected `inputs` to have size "
+          << input_names.size() << " but got size " << inputs.size() << "."
+          << std::endl;
+      throw std::invalid_argument(msg.str());
+    }
+    if (output_shapes.size() != output_names.size()) {
+      std::ostringstream msg;
+      msg << "[custom_kernel] Expected `output_shapes` to have size "
+          << output_names.size() << " but got size " << output_shapes.size()
+          << "." << std::endl;
+      throw std::invalid_argument(msg.str());
+    }
+    if (output_dtypes.size() != output_names.size()) {
+      std::ostringstream msg;
+      msg << "[custom_kernel] Expected `output_dtypes` to have size "
+          << output_names.size() << " but got size " << output_dtypes.size()
+          << "." << std::endl;
+      throw std::invalid_argument(msg.str());
+    }
+
+    auto s = to_stream(s_);
+    if (s.device != Device::gpu) {
+      throw std::invalid_argument("[custom_kernel] Only supports the GPU.");
+    }
+
+    std::string kernel_name =
+        "custom_kernel_" + name + template_arguments_hash(template_args);
+    std::string kernel_source = build_kernel(
+        kernel_name,
+        header,
+        source,
+        input_names,
+        inputs,
+        output_names,
+        output_dtypes,
+        template_args,
+        shape_infos);
+
+    if (verbose) {
+      std::cout << "Generated source code for `" << kernel_name
+                << "`:" << std::endl
+                << "```" << std::endl
+                << kernel_source << std::endl
+                << "```" << std::endl;
+    }
+
+    return array::make_arrays(
+        std::move(output_shapes),
+        std::move(output_dtypes),
+        std::make_shared<CustomKernel>(
+            s,
+            std::move(kernel_name),
+            std::move(kernel_source),
+            grid,
+            threadgroup,
+            shape_infos,
+            ensure_row_contiguous,
+            init_value,
+            std::vector<ScalarArg>{},
+            false,
+            shared_memory),
+        std::move(inputs));
+  };
+}
+
+std::vector<array> precompiled_cuda_kernel(
+    const std::string& name,
+    const std::string& compiled_source,
+    const std::vector<array>& inputs,
+    const std::vector<Shape>& output_shapes,
+    const std::vector<Dtype>& output_dtypes,
+    const std::vector<ScalarArg>& scalars,
+    std::tuple<int, int, int> grid,
+    std::tuple<int, int, int> threadgroup,
+    int shared_memory,
+    std::optional<float> init_value,
+    bool ensure_row_contiguous,
+    StreamOrDevice s) {
+  std::vector<CustomKernelShapeInfo> shape_infos(
+      inputs.size(), CustomKernelShapeInfo{false, false, false});
+  return array::make_arrays(
+      output_shapes,
+      output_dtypes,
+      std::make_shared<CustomKernel>(
+          to_stream(s),
+          name,
+          compiled_source,
+          grid,
+          threadgroup,
+          shape_infos,
+          ensure_row_contiguous,
+          init_value,
+          scalars,
+          true,
+          shared_memory),
+      inputs);
+}
+
+void CustomKernel::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  nvtx3::scoped_range r("CustomKernel::eval_gpu");
+  auto& s = stream();
+
+  std::vector<array> copies;
+
+  // Allocate and initialize the output arrays
+  for (auto& out : outputs) {
+    if (init_value_) {
+      copies.emplace_back(init_value_.value(), out.dtype());
+      fill_gpu(copies.back(), out, s);
+    } else {
+      out.set_data(allocator::malloc(out.nbytes()));
+    }
+  }
+
+  // Create the input arrays and copy if needed
+  auto check_input = [&copies, &s, this](const array& x) -> const array {
+    bool no_copy = x.flags().row_contiguous;
+    if (!ensure_row_contiguous_ || no_copy) {
+      return x;
+    } else {
+      copies.push_back(array(x.shape(), x.dtype(), nullptr, {}));
+      copy_gpu(x, copies.back(), CopyType::General, s);
+      return copies.back();
+    }
+  };
+  std::vector<array> checked_inputs;
+  for (const array& in : inputs) {
+    checked_inputs.push_back(check_input(in));
+  }
+
+  // Compile the custom kernel
+  std::string kernel_name =
+      (is_precompiled_) ? name_ : "mlx::core::cu::" + name_;
+  cu::JitModule& mod = cu::get_jit_module(
+      s.device,
+      name_,
+      [&]() {
+        return std::make_tuple(
+            is_precompiled_, source_, std::vector{kernel_name});
+      },
+      false);
+
+  // Make the arguments
+  cu::KernelArgs args;
+  for (int i = 0; i < checked_inputs.size(); i++) {
+    const array& in = checked_inputs[i];
+    auto& shape_info = shape_infos_[i];
+    args.append(in);
+    if (shape_info.shape) {
+      args.append_ndim(in.shape());
+    }
+    if (shape_info.strides) {
+      args.append_ndim(in.strides());
+    }
+    if (shape_info.ndim) {
+      args.append<int32_t>(in.ndim());
+    }
+  }
+  for (auto& out : outputs) {
+    args.append(out);
+  }
+  for (auto& s : scalar_arguments_) {
+    if (std::holds_alternative<bool>(s)) {
+      args.append(std::get<bool>(s));
+    } else if (std::holds_alternative<int>(s)) {
+      args.append(std::get<int>(s));
+    } else if (std::holds_alternative<float>(s)) {
+      args.append(std::get<float>(s));
+    }
+  }
+
+  // Make the grid
+  const auto [tx, ty, tz] = threadgroup_;
+  const auto [gx, gy, gz] = grid_;
+  dim3 block(std::min(tx, gx), std::min(ty, gy), std::min(tz, gz));
+  dim3 grid((gx + tx - 1) / tx, (gy + ty - 1) / ty, (gz + tz - 1) / tz);
+
+  // Call the kernel
+  auto& encoder = cu::get_command_encoder(s);
+  for (const auto& in : checked_inputs) {
+    encoder.set_input_array(in);
+  }
+  for (const auto& out : outputs) {
+    encoder.set_output_array(out);
+  }
+  for (const auto& t : copies) {
+    encoder.add_temporary(t);
+  }
+  auto kernel =
+      mod.get_kernel(kernel_name, [smem = shared_memory_](CUfunction kernel) {
+        if (smem > 0 && smem > 48000) {
+          cuFuncSetAttribute(
+              kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, smem);
+        }
+      });
+  encoder.add_kernel_node(kernel, grid, block, shared_memory_, args.args());
+}
+
+} // namespace mlx::core::fast

mlx/backend/cuda/device.cpp

@@ -1,6 +1,7 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/jit_module.h"
 #include "mlx/backend/cuda/worker.h"
 #include "mlx/utils.h"
 
@@ -9,12 +10,23 @@
 #include <future>
 #include <unordered_set>
 
-namespace mlx::core {
+namespace mlx::core::cu {
+
+namespace {
 
 // Can be tuned with MLX_MAX_OPS_PER_BUFFER
 // This should be less than 255
 constexpr int default_max_nodes_per_graph = 20;
 
+#define CHECK_CUDNN_ERROR(cmd) check_cudnn_error(#cmd, (cmd))
+
+void check_cudnn_error(const char* name, cudnnStatus_t err) {
+  if (err != CUDNN_STATUS_SUCCESS) {
+    throw std::runtime_error(
+        fmt::format("{} failed: {}.", name, cudnnGetErrorString(err)));
+  }
+}
+
 int cuda_graph_cache_size() {
   static int cache_size = []() {
     return env::get_var("MLX_CUDA_GRAPH_CACHE_SIZE", 100);
@@ -22,7 +34,7 @@ int cuda_graph_cache_size() {
   return cache_size;
 }
 
-namespace cu {
+} // namespace
 
 Device::Device(int device) : device_(device) {
   CHECK_CUDA_ERROR(cudaDeviceGetAttribute(
@@ -40,11 +52,18 @@ Device::Device(int device) : device_(device) {
   }
   // The cublasLt handle is used by matmul.
   make_current();
-  cublasLtCreate(&lt_);
+  CHECK_CUBLAS_ERROR(cublasLtCreate(&lt_));
+  // The cudnn handle is used by Convolution.
+  CHECK_CUDNN_ERROR(cudnnCreate(&cudnn_));
+
+  // Initialize the jit module cache here ensures it is not
+  // unloaded before any evaluation is done
+  get_jit_module_cache();
 }
 
 Device::~Device() {
-  cublasLtDestroy(lt_);
+  CHECK_CUDNN_ERROR(cudnnDestroy(cudnn_));
+  CHECK_CUBLAS_ERROR(cublasLtDestroy(lt_));
 }
 
 void Device::make_current() {
@@ -66,29 +85,17 @@ CommandEncoder& Device::get_command_encoder(Stream s) {
 }
 
 CommandEncoder::CaptureContext::CaptureContext(CommandEncoder& enc) : enc(enc) {
+  enc.device().make_current();
   CHECK_CUDA_ERROR(
       cudaStreamBeginCapture(enc.stream(), cudaStreamCaptureModeGlobal));
 }
 
 CommandEncoder::CaptureContext::~CaptureContext() {
-  CHECK_CUDA_ERROR(cudaStreamEndCapture(enc.stream(), &graph));
-  size_t num_nodes;
-  CHECK_CUDA_ERROR(cudaGraphGetNodes(graph, NULL, &num_nodes));
-  if (num_nodes == 1) {
-    cudaGraphNode_t captured_node;
-    CHECK_CUDA_ERROR(cudaGraphGetNodes(graph, &captured_node, &num_nodes));
-    CUDA_KERNEL_NODE_PARAMS params;
-    CHECK_CUDA_ERROR(cuGraphKernelNodeGetParams(captured_node, &params));
-    cudaGraphNode_t node;
-    CHECK_CUDA_ERROR(cuGraphAddKernelNode(&node, enc.graph_, NULL, 0, &params));
-    enc.insert_graph_dependencies(GraphNode{node, 'K'});
-  } else {
-    cudaGraphNode_t node;
-    CHECK_CUDA_ERROR(
-        cudaGraphAddChildGraphNode(&node, enc.graph_, NULL, 0, graph));
-    enc.insert_graph_dependencies(GraphNode{node, 'G'});
+  graph.end_capture(enc.stream());
+  if (discard) {
+    return;
   }
-  CHECK_CUDA_ERROR(cudaGraphDestroy(graph));
+  enc.add_graph_node(graph);
 }
 
 CommandEncoder::ConcurrentContext::ConcurrentContext(CommandEncoder& enc)
@@ -175,20 +182,11 @@ void CommandEncoder::insert_graph_dependencies(std::vector<GraphNode> nodes) {
   }
 }
 
-CommandEncoder::CommandEncoder(Device& d) : device_(d), stream_(d) {
-  CHECK_CUDA_ERROR(cudaGraphCreate(&graph_, 0));
-}
-
-void clear_graphs(std::unordered_map<std::string, cudaGraphExec_t>& graphs) {
-  for (auto& [_, graph_exec] : graphs) {
-    CHECK_CUDA_ERROR(cudaGraphExecDestroy(graph_exec));
-  }
-  graphs.clear();
-}
-
-CommandEncoder::~CommandEncoder() {
-  clear_graphs(graph_cache_);
-}
+CommandEncoder::CommandEncoder(Device& d)
+    : device_(d),
+      stream_(d),
+      graph_(d),
+      graph_cache_(cuda_graph_cache_size()) {}
 
 void CommandEncoder::add_completed_handler(std::function<void()> task) {
   worker_.add_task(std::move(task));
@@ -215,22 +213,22 @@ void CommandEncoder::add_kernel_node(
     void* func,
     dim3 grid_dim,
     dim3 block_dim,
+    uint32_t smem_bytes,
     void** params) {
   cudaKernelNodeParams kernel_params = {0};
   kernel_params.func = func;
   kernel_params.gridDim = grid_dim;
   kernel_params.blockDim = block_dim;
   kernel_params.kernelParams = params;
-  cudaGraphNode_t node;
-  CHECK_CUDA_ERROR(
-      cudaGraphAddKernelNode(&node, graph_, NULL, 0, &kernel_params));
-  insert_graph_dependencies(GraphNode{node, 'K'});
+  kernel_params.sharedMemBytes = smem_bytes;
+  add_kernel_node(kernel_params);
 }
 
 void CommandEncoder::add_kernel_node(
     CUfunction func,
     dim3 grid_dim,
     dim3 block_dim,
+    uint32_t smem_bytes,
     void** params) {
   CUDA_KERNEL_NODE_PARAMS kernel_params = {0};
   kernel_params.func = func;
@@ -241,13 +239,30 @@ void CommandEncoder::add_kernel_node(
   kernel_params.blockDimY = block_dim.y;
   kernel_params.blockDimZ = block_dim.z;
   kernel_params.kernelParams = params;
-  CUgraphNode node;
-  CHECK_CUDA_ERROR(
-      cuGraphAddKernelNode(&node, graph_, NULL, 0, &kernel_params));
+  kernel_params.sharedMemBytes = smem_bytes;
+  add_kernel_node(kernel_params);
+}
+
+void CommandEncoder::add_kernel_node(const cudaKernelNodeParams& params) {
+  cudaGraphNode_t node;
+  CHECK_CUDA_ERROR(cudaGraphAddKernelNode(&node, graph_, NULL, 0, &params));
   insert_graph_dependencies(GraphNode{node, 'K'});
 }
 
+void CommandEncoder::add_kernel_node(const CUDA_KERNEL_NODE_PARAMS& params) {
+  CUgraphNode node;
+  CHECK_CUDA_ERROR(cuGraphAddKernelNode(&node, graph_, NULL, 0, &params));
+  insert_graph_dependencies(GraphNode{node, 'K'});
+}
+
+void CommandEncoder::add_graph_node(cudaGraph_t child) {
+  cudaGraphNode_t node;
+  CHECK_CUDA_ERROR(cudaGraphAddChildGraphNode(&node, graph_, NULL, 0, child));
+  insert_graph_dependencies(GraphNode{node, 'G'});
+}
+
 void CommandEncoder::commit() {
+  nvtx3::scoped_range r("CommandEncoder::commit");
   if (!temporaries_.empty()) {
     add_completed_handler([temporaries = std::move(temporaries_)]() {});
   }
@@ -264,7 +279,7 @@ void CommandEncoder::commit() {
     graph_key_ += ".";
     graph_key_ += std::to_string(empty_node_count_);
 
-    cudaGraphExec_t& graph_exec = graph_cache_[graph_key_];
+    CudaGraphExec& graph_exec = graph_cache_[graph_key_];
 
     if (graph_exec != nullptr) {
       cudaGraphExecUpdateResult update_result;
@@ -278,35 +293,27 @@ void CommandEncoder::commit() {
 #endif // CUDART_VERSION >= 12000
       if (update_result != cudaGraphExecUpdateSuccess) {
         cudaGetLastError(); // reset error
-        CHECK_CUDA_ERROR(cudaGraphExecDestroy(graph_exec));
-        graph_exec = nullptr;
+        graph_exec.reset();
       }
     }
     if (graph_exec == nullptr) {
-      CHECK_CUDA_ERROR(
-          cudaGraphInstantiate(&graph_exec, graph_, NULL, NULL, 0));
+      graph_exec.instantiate(graph_);
     }
     device_.make_current();
     CHECK_CUDA_ERROR(cudaGraphLaunch(graph_exec, stream_));
 
-    // TODO smarter cache policy
-    if (graph_cache_.size() > cuda_graph_cache_size()) {
-      clear_graphs(graph_cache_);
-    }
-
     // Reset state
     node_count_ = 0;
     graph_node_count_ = 0;
+    empty_node_count_ = 0;
     from_nodes_.clear();
     to_nodes_.clear();
     graph_key_.clear();
     node_map_.clear();
-    CHECK_CUDA_ERROR(cudaGraphDestroy(graph_));
-    CHECK_CUDA_ERROR(cudaGraphCreate(&graph_, 0));
+    graph_ = CudaGraph(device_);
   }
 
   // Put completion handlers in a batch.
-  worker_.end_batch();
   worker_.commit(stream_);
 }
 
@@ -315,7 +322,6 @@ void CommandEncoder::synchronize() {
   auto p = std::make_shared<std::promise<void>>();
   std::future<void> f = p->get_future();
   add_completed_handler([p = std::move(p)]() { p->set_value(); });
-  worker_.end_batch();
   commit();
   f.wait();
 }
@@ -333,6 +339,4 @@ CommandEncoder& get_command_encoder(Stream s) {
   return device(s.device).get_command_encoder(s);
 }
 
-} // namespace cu
-
-} // namespace mlx::core
+} // namespace mlx::core::cu

mlx/backend/cuda/device.h

@@ -3,11 +3,13 @@
 #pragma once
 
 #include "mlx/array.h"
+#include "mlx/backend/cuda/lru_cache.h"
 #include "mlx/backend/cuda/worker.h"
 #include "mlx/stream.h"
 
 #include <cublasLt.h>
 #include <cuda.h>
+#include <cudnn.h>
 #include <thrust/execution_policy.h>
 
 #include <unordered_map>
@@ -19,8 +21,9 @@ class CommandEncoder {
   struct CaptureContext {
     CaptureContext(CommandEncoder& enc);
     ~CaptureContext();
-    cudaGraph_t graph;
+    CudaGraph graph;
     CommandEncoder& enc;
+    bool discard{false};
   };
   struct ConcurrentContext {
     ConcurrentContext(CommandEncoder& enc);
@@ -29,7 +32,6 @@ class CommandEncoder {
   };
 
   explicit CommandEncoder(Device& d);
-  ~CommandEncoder();
 
   CommandEncoder(const CommandEncoder&) = delete;
   CommandEncoder& operator=(const CommandEncoder&) = delete;
@@ -45,25 +47,39 @@ class CommandEncoder {
   void set_output_array(const array& arr);
 
   template <typename F, typename... Params>
-  void
-  add_kernel_node(F* func, dim3 grid_dim, dim3 block_dim, Params&&... params) {
+  void add_kernel_node(
+      F* func,
+      dim3 grid_dim,
+      dim3 block_dim,
+      uint32_t smem_bytes,
+      Params&&... params) {
     constexpr size_t num = sizeof...(Params);
     void* ptrs[num];
     size_t i = 0;
     ([&](auto&& p) { ptrs[i++] = static_cast<void*>(&p); }(
          std::forward<Params>(params)),
      ...);
-    add_kernel_node((void*)func, grid_dim, block_dim, ptrs);
+    add_kernel_node((void*)func, grid_dim, block_dim, smem_bytes, ptrs);
   }
 
   void add_kernel_node(
       CUfunction func,
       dim3 grid_dim,
       dim3 block_dim,
+      uint32_t smem_bytes,
       void** params);
 
-  void
-  add_kernel_node(void* func, dim3 grid_dim, dim3 block_dim, void** params);
+  void add_kernel_node(
+      void* func,
+      dim3 grid_dim,
+      dim3 block_dim,
+      uint32_t smem_bytes,
+      void** params);
+
+  // Low-level graph helpers.
+  void add_kernel_node(const cudaKernelNodeParams& params);
+  void add_kernel_node(const CUDA_KERNEL_NODE_PARAMS& params);
+  void add_graph_node(cudaGraph_t child);
 
   void add_temporary(const array& arr) {
     temporaries_.push_back(arr.data_shared_ptr());
@@ -73,6 +89,10 @@ class CommandEncoder {
   void maybe_commit();
   void commit();
 
+  Device& device() {
+    return device_;
+  }
+
   CudaStream& stream() {
     return stream_;
   }
@@ -95,7 +115,7 @@ class CommandEncoder {
 
   Device& device_;
   CudaStream stream_;
-  cudaGraph_t graph_;
+  CudaGraph graph_;
   Worker worker_;
   char node_count_{0};
   char graph_node_count_{0};
@@ -106,7 +126,7 @@ class CommandEncoder {
   std::string graph_key_;
   std::vector<GraphNode> concurrent_nodes_;
   std::vector<std::shared_ptr<array::Data>> temporaries_;
-  std::unordered_map<std::string, cudaGraphExec_t> graph_cache_;
+  LRUCache<std::string, CudaGraphExec> graph_cache_;
   std::vector<std::uintptr_t> active_deps_;
   std::vector<std::uintptr_t> active_outputs_;
   std::unordered_map<std::uintptr_t, GraphNode> node_map_;
@@ -137,12 +157,16 @@ class Device {
   cublasLtHandle_t lt_handle() const {
     return lt_;
   }
+  cudnnHandle_t cudnn_handle() const {
+    return cudnn_;
+  }
 
  private:
   int device_;
   int compute_capability_major_;
   int compute_capability_minor_;
   cublasLtHandle_t lt_;
+  cudnnHandle_t cudnn_;
   std::unordered_map<int, CommandEncoder> encoders_;
 };
 

mlx/backend/cuda/device/arange.cuh

@@ -1,15 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-namespace mlx::core::cu {
-
-template <typename T>
-struct Arange {
-  const T start;
-  const T step;
-
-  __device__ T operator()(uint32_t i) const {
-    return start + i * step;
-  }
-};
-
-} // namespace mlx::core::cu

mlx/backend/cuda/device/atomic_ops.cuh

@@ -49,11 +49,7 @@ inline __device__ void atomic_add(__half* out, __half val) {
 }
 
 inline __device__ void atomic_add(complex64_t* out, complex64_t val) {
-#if __CUDA_ARCH__ < 900
   atomic_add_general(out, val);
-#else
-  atomicAdd(out, val);
-#endif
 }
 
 inline __device__ void atomic_add(__nv_bfloat16* out, __nv_bfloat16 val) {

mlx/backend/cuda/device/utils.cuh

@@ -32,23 +32,137 @@ using Strides = cuda::std::array<int64_t, MAX_NDIM>;
 template <typename T, int N>
 struct alignas(sizeof(T) * N) AlignedVector {
   T val[N];
+
+  __device__ T& operator[](int i) {
+    return val[i];
+  }
+
+  __device__ T operator[](int i) const {
+    return val[i];
+  }
 };
 
 template <int N, typename T>
-inline __device__ AlignedVector<T, N> load_vector(
+inline __host__ __device__ bool is_aligned(T* x) {
+  return (reinterpret_cast<uintptr_t>(x) % (N * sizeof(T))) == 0;
+}
+
+template <int N, typename T>
+inline __device__ AlignedVector<T, N> unsafe_load_vector(
     const T* ptr,
     uint32_t offset) {
   auto* from = reinterpret_cast<const AlignedVector<T, N>*>(ptr);
   return from[offset];
 }
 
+template <int N, typename T>
+inline __device__ AlignedVector<T, N> load_vector(
+    const T* ptr,
+    uint32_t offset) {
+  if (is_aligned<N>(ptr)) {
+    auto* from = reinterpret_cast<const AlignedVector<T, N>*>(ptr);
+    return from[offset];
+  } else {
+    AlignedVector<T, N> v;
+#pragma unroll
+    for (int i = 0; i < N; ++i) {
+      v[i] = ptr[offset * N + i];
+    }
+    return v;
+  }
+}
+
+template <int N, typename T, typename SizeT>
+inline __device__ AlignedVector<T, N>
+load_vector(const T* ptr, uint32_t offset, SizeT size, T fallback) {
+  if (is_aligned<N>(ptr) && (offset + 1) * N <= size) {
+    auto* from = reinterpret_cast<const AlignedVector<T, N>*>(ptr);
+    return from[offset];
+  } else {
+    AlignedVector<T, N> v;
+#pragma unroll
+    for (int i = 0; i < N; ++i) {
+      v[i] = (N * offset + i) < size ? ptr[offset * N + i] : fallback;
+    }
+    return v;
+  }
+}
+
+template <int N, typename T, typename SizeT>
+inline __device__ AlignedVector<T, N> load_vector(
+    const T* ptr,
+    uint32_t offset,
+    SizeT size,
+    int64_t stride,
+    T fallback) {
+  if (is_aligned<N>(ptr) && stride == 1 && (offset + 1) * N <= size) {
+    auto* from = reinterpret_cast<const AlignedVector<T, N>*>(ptr);
+    return from[offset];
+  } else {
+    AlignedVector<T, N> v;
+#pragma unroll
+    for (int i = 0; i < N; ++i) {
+      v[i] =
+          (N * offset + i) < size ? ptr[stride * (offset * N + i)] : fallback;
+    }
+    return v;
+  }
+}
+
 template <int N, typename T>
 inline __device__ void
-store_vector(T* ptr, uint32_t offset, const AlignedVector<T, N>& vec) {
+unsafe_store_vector(T* ptr, uint32_t offset, const AlignedVector<T, N>& vec) {
   auto* to = reinterpret_cast<AlignedVector<T, N>*>(ptr);
   to[offset] = vec;
 }
 
+template <int N, typename T>
+inline __device__ void
+store_vector(T* ptr, uint32_t offset, const AlignedVector<T, N>& vec) {
+  if (is_aligned<N>(ptr)) {
+    auto* to = reinterpret_cast<AlignedVector<T, N>*>(ptr);
+    to[offset] = vec;
+  } else {
+#pragma unroll
+    for (int i = 0; i < N; ++i) {
+      ptr[offset * N + i] = vec[i];
+    }
+  }
+}
+
+template <int N, typename T, typename SizeT>
+inline __device__ void store_vector(
+    T* ptr,
+    uint32_t offset,
+    const AlignedVector<T, N>& vec,
+    SizeT size) {
+  if (is_aligned<N>(ptr) && (offset + 1) * N <= size) {
+    auto* to = reinterpret_cast<AlignedVector<T, N>*>(ptr);
+    to[offset] = vec;
+  } else {
+    for (int i = 0; (offset * N + i) < size && i < N; ++i) {
+      ptr[offset * N + i] = vec[i];
+    }
+  }
+}
+
+template <int N, typename T, typename SizeT>
+inline __device__ void store_vector(
+    T* ptr,
+    uint32_t offset,
+    const AlignedVector<T, N>& vec,
+    SizeT size,
+    int64_t stride) {
+  if (is_aligned<N>(ptr) && (offset + 1) * N <= size && stride == 1) {
+    auto* to = reinterpret_cast<AlignedVector<T, N>*>(ptr);
+    to[offset] = vec;
+  } else {
+    for (int i = 0; (offset * N + i) < size && i < N; ++i) {
+      ptr[stride * (offset * N + i)] = vec[i];
+    }
+  }
+}
+
 ///////////////////////////////////////////////////////////////////////////////
 // Type limits utils
 ///////////////////////////////////////////////////////////////////////////////
@@ -204,20 +318,8 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_nd(
   return cuda::std::make_tuple(a_loc, b_loc, c_loc);
 }
 
-// Optimized version when ndim is larger than 4.
 template <typename IdxT = int64_t>
-inline __host__ __device__ IdxT
-elem_to_loc_4d(IdxT elem, const int* shape, const int64_t* strides, int ndim) {
-  IdxT loc = 0;
-  for (int i = ndim - 1; i >= 0; --i) {
-    loc += (elem % shape[i]) * IdxT(strides[i]);
-    elem /= shape[i];
-  }
-  return loc;
-}
-
-template <typename IdxT = int64_t>
-inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_4d(
+inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc(
     IdxT elem,
     const int* shape,
     const int64_t* a_strides,
@@ -235,7 +337,7 @@ inline __host__ __device__ cuda::std::tuple<IdxT, IdxT> elem_to_loc_4d(
 }
 
 template <typename IdxT = int64_t>
-inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc_4d(
+inline __host__ __device__ cuda::std::tuple<IdxT, IdxT, IdxT> elem_to_loc(
     IdxT elem,
     const int* shape,
     const int64_t* a_strides,

mlx/backend/cuda/eval.cpp

@@ -19,8 +19,6 @@ void new_stream(Stream s) {
   cudaFree(nullptr);
   // Ensure the static stream objects get created.
   cu::get_command_encoder(s);
-  // The main thread is safe to free buffers.
-  cu::allocator().register_this_thread();
 }
 
 void eval(array& arr) {
@@ -38,18 +36,15 @@ void eval(array& arr) {
 
   auto& encoder = cu::get_command_encoder(arr.primitive().stream());
   // Keep used buffers alive until kernel finishes running.
-  std::unordered_set<std::shared_ptr<array::Data>> buffers;
   for (auto& in : arr.inputs()) {
-    buffers.insert(in.data_shared_ptr());
+    // Except for the donated one.
+    if (in.data_shared_ptr() != arr.data_shared_ptr()) {
+      encoder.add_temporary(in);
+    }
   }
   for (auto& s : arr.siblings()) {
-    buffers.insert(s.data_shared_ptr());
+    encoder.add_temporary(s);
   }
-  // Remove the output if it was donated to by an input.
-  if (auto it = buffers.find(arr.data_shared_ptr()); it != buffers.end()) {
-    buffers.erase(it);
-  }
-  encoder.add_completed_handler([buffers = std::move(buffers)]() {});
   encoder.maybe_commit();
 }
 

mlx/backend/cuda/event.cu

@@ -110,24 +110,26 @@ __global__ void event_signal_kernel(SharedEvent::Atomic* ac, uint64_t value) {
   event_signal(ac, value);
 }
 
+SharedEvent::Atomic* to_atomic(std::shared_ptr<Buffer> buf) {
+  return static_cast<SharedEvent::Atomic*>(buf->raw_ptr());
+}
+
 SharedEvent::SharedEvent() {
-  // Allocate cuda::atomic on managed memory.
-  Atomic* ac;
-  CHECK_CUDA_ERROR(cudaMallocManaged(&ac, sizeof(Atomic)));
-  new (ac) Atomic(0);
-  ac_ = std::shared_ptr<Atomic>(ac, [](Atomic* ptr) {
-    ptr->~Atomic();
-    allocator().cuda_free(ptr);
-  });
+  buf_ = std::shared_ptr<Buffer>(
+      new Buffer{allocator().malloc(sizeof(Atomic))}, [](Buffer* ptr) {
+        allocator().free(*ptr);
+        delete ptr;
+      });
+  *static_cast<uint64_t*>(buf_->raw_ptr()) = 0;
 }
 
 void SharedEvent::wait(uint64_t value) {
   nvtx3::scoped_range r("cu::SharedEvent::wait");
-  event_wait(ac_.get(), value);
+  event_wait(to_atomic(buf_), value);
 }
 
 void SharedEvent::wait(cudaStream_t stream, uint64_t value) {
-  event_wait_kernel<<<1, 1, 0, stream>>>(ac_.get(), value);
+  event_wait_kernel<<<1, 1, 0, stream>>>(to_atomic(buf_), value);
 }
 
 void SharedEvent::wait(Stream s, uint64_t value) {
@@ -138,17 +140,17 @@ void SharedEvent::wait(Stream s, uint64_t value) {
     auto& encoder = get_command_encoder(s);
     encoder.commit();
     wait(encoder.stream(), value);
-    encoder.add_completed_handler([ac = ac_]() {});
+    encoder.add_completed_handler([buf = buf_]() {});
   }
 }
 
 void SharedEvent::signal(uint64_t value) {
   nvtx3::scoped_range r("cu::SharedEvent::signal");
-  event_signal(ac_.get(), value);
+  event_signal(to_atomic(buf_), value);
 }
 
 void SharedEvent::signal(cudaStream_t stream, uint64_t value) {
-  event_signal_kernel<<<1, 1, 0, stream>>>(ac_.get(), value);
+  event_signal_kernel<<<1, 1, 0, stream>>>(to_atomic(buf_), value);
 }
 
 void SharedEvent::signal(Stream s, uint64_t value) {
@@ -162,18 +164,18 @@ void SharedEvent::signal(Stream s, uint64_t value) {
     auto& encoder = get_command_encoder(s);
     encoder.commit();
     signal(encoder.stream(), value);
-    encoder.add_completed_handler([ac = ac_]() {});
+    encoder.add_completed_handler([buf = buf_]() {});
   }
 }
 
 bool SharedEvent::is_signaled(uint64_t value) const {
   nvtx3::scoped_range r("cu::SharedEvent::is_signaled");
-  return ac_->load() >= value;
+  return to_atomic(buf_)->load() >= value;
 }
 
 uint64_t SharedEvent::value() const {
   nvtx3::scoped_range r("cu::SharedEvent::value");
-  return ac_->load();
+  return to_atomic(buf_)->load();
 }
 
 } // namespace cu

mlx/backend/cuda/event.h

@@ -2,6 +2,7 @@
 
 #pragma once
 
+#include "mlx/allocator.h"
 #include "mlx/stream.h"
 
 #include <cuda_runtime.h>
@@ -55,12 +56,8 @@ class SharedEvent {
   bool is_signaled(uint64_t value) const;
   uint64_t value() const;
 
-  const std::shared_ptr<Atomic>& atomic() const {
-    return ac_;
-  }
-
  private:
-  std::shared_ptr<Atomic> ac_;
+  std::shared_ptr<mlx::core::allocator::Buffer> buf_;
 };
 
 } // namespace mlx::core::cu

mlx/backend/cuda/gemms/cublas_gemm.cpp

@@ -0,0 +1,348 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/gemms/cublas_gemm.h"
+#include "mlx/backend/cuda/device.h"
+#include "mlx/dtype_utils.h"
+#include "mlx/utils.h"
+
+#include <fmt/format.h>
+
+namespace mlx::core {
+
+namespace {
+
+struct CublasPreference {
+  CublasPreference(cu::Device& device) {
+    // The recommended cublas workspace size is 4 MiB for pre-Hopper and 32 MiB
+    // for Hopper+:
+    // https://docs.nvidia.com/cuda/cublas/#cublassetworkspace
+    uint64_t MiB = 1024 * 1024;
+    uint64_t workspace_size =
+        device.compute_capability_major() >= 9 ? 32 * MiB : 4 * MiB;
+
+    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceCreate(&pref_));
+    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceSetAttribute(
+        pref_,
+        CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
+        &workspace_size,
+        sizeof(uint64_t)));
+  }
+
+  ~CublasPreference() {
+    CHECK_CUBLAS_ERROR(cublasLtMatmulPreferenceDestroy(pref_));
+  }
+
+  cublasLtMatmulPreference_t pref_{nullptr};
+};
+
+cublasLtMatmulPreference_t cublas_preference(cu::Device& device) {
+  static CublasPreference pref(device);
+  return pref.pref_;
+}
+
+cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
+  switch (dtype) {
+    case float16:
+      return CUBLAS_COMPUTE_32F;
+    case bfloat16:
+      return CUBLAS_COMPUTE_32F;
+    case float32:
+      return mlx::core::env::enable_tf32() ? CUBLAS_COMPUTE_32F_FAST_TF32
+                                           : CUBLAS_COMPUTE_32F;
+    case float64:
+    case complex64:
+      return CUBLAS_COMPUTE_64F;
+    default:
+      throw std::runtime_error(fmt::format(
+          "Unsupported dtype in CublasGemm: {}.", dtype_to_string(dtype)));
+  }
+}
+
+cudaDataType_t dtype_to_cublas_type(Dtype dtype) {
+  switch (dtype) {
+    case float16:
+      return CUDA_R_16F;
+    case bfloat16:
+      return CUDA_R_16BF;
+    case float32:
+      return CUDA_R_32F;
+    case float64:
+      return CUDA_R_64F;
+    case complex64:
+      return CUDA_C_32F;
+    default:
+      throw std::runtime_error(fmt::format(
+          "Unsupported dtype in CublasGemm: {}.", dtype_to_string(dtype)));
+  }
+}
+
+cublasLtMatrixLayout_t create_matrix_layout(
+    cudaDataType_t type,
+    uint64_t rows,
+    uint64_t cols,
+    bool transposed,
+    int64_t ld,
+    int32_t batch_count,
+    int64_t batch_stride) {
+  cublasLtMatrixLayout_t desc;
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutCreate(&desc, type, rows, cols, ld));
+  cublasLtOrder_t order = transposed ? CUBLASLT_ORDER_COL : CUBLASLT_ORDER_ROW;
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+      desc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order, sizeof(cublasLtOrder_t)));
+  if (batch_count > 1) {
+    CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+        desc,
+        CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT,
+        &batch_count,
+        sizeof(int32_t)));
+    CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+        desc,
+        CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET,
+        &batch_stride,
+        sizeof(int64_t)));
+  }
+  return desc;
+}
+
+} // namespace
+
+CublasGemm::CublasGemm(
+    cu::Device& device,
+    Dtype dtype,
+    bool a_transposed,
+    uint64_t a_rows,
+    uint64_t a_cols,
+    int64_t lda,
+    bool b_transposed,
+    uint64_t b_rows,
+    uint64_t b_cols,
+    int64_t ldb,
+    int32_t batch_count,
+    int64_t a_batch_stride,
+    int64_t b_batch_stride)
+    : handle_(device.lt_handle()),
+      pref_(cublas_preference(device)),
+      M_(a_rows),
+      N_(b_cols) {
+  heuristic_.state = CUBLAS_STATUS_NOT_INITIALIZED;
+
+  auto scale_type = dtype_to_cublas_type(dtype);
+  if (dtype == bfloat16 || dtype == float16) {
+    scale_type = CUDA_R_32F;
+  }
+  CHECK_CUBLAS_ERROR(cublasLtMatmulDescCreate(
+      &matmul_desc_, dtype_to_compute_type(dtype), scale_type));
+  int32_t pointer_mode = CUBLASLT_POINTER_MODE_HOST;
+  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+      matmul_desc_,
+      CUBLASLT_MATMUL_DESC_POINTER_MODE,
+      &pointer_mode,
+      sizeof(int32_t)));
+  cublasOperation_t op = CUBLAS_OP_N;
+  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+      matmul_desc_,
+      CUBLASLT_MATMUL_DESC_TRANSA,
+      &op,
+      sizeof(cublasOperation_t)));
+  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
+      matmul_desc_,
+      CUBLASLT_MATMUL_DESC_TRANSB,
+      &op,
+      sizeof(cublasOperation_t)));
+
+  auto type = dtype_to_cublas_type(dtype);
+  a_desc_ = create_matrix_layout(
+      type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
+  b_desc_ = create_matrix_layout(
+      type, b_rows, b_cols, b_transposed, ldb, batch_count, b_batch_stride);
+  out_desc_ = create_matrix_layout(
+      type, a_rows, b_cols, false, b_cols, batch_count, a_rows * b_cols);
+}
+
+CublasGemm::CublasGemm(
+    cu::Device& device,
+    Dtype dtype,
+    bool a_transposed,
+    uint64_t a_rows,
+    uint64_t a_cols,
+    int64_t lda,
+    bool b_transposed,
+    uint64_t b_rows,
+    uint64_t b_cols,
+    int64_t ldb,
+    int64_t ldc,
+    int32_t batch_count,
+    int64_t a_batch_stride,
+    int64_t b_batch_stride,
+    int64_t c_batch_stride)
+    : CublasGemm(
+          device,
+          dtype,
+          a_transposed,
+          a_rows,
+          a_cols,
+          lda,
+          b_transposed,
+          b_rows,
+          b_cols,
+          ldb,
+          batch_count,
+          a_batch_stride,
+          b_batch_stride) {
+  auto type = dtype_to_cublas_type(dtype);
+  c_desc_ = create_matrix_layout(
+      type, a_rows, b_cols, false, ldc, batch_count, c_batch_stride);
+}
+
+CublasGemm::~CublasGemm() {
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(a_desc_));
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(b_desc_));
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(c_desc_));
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(out_desc_));
+  CHECK_CUBLAS_ERROR(cublasLtMatmulDescDestroy(matmul_desc_));
+}
+
+void CublasGemm::set_out(
+    Dtype dtype,
+    bool transposed,
+    uint64_t rows,
+    uint64_t cols,
+    int64_t ld,
+    int32_t batch_count,
+    int64_t batch_stride) {
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(out_desc_));
+  out_desc_ = create_matrix_layout(
+      dtype_to_cublas_type(dtype),
+      rows,
+      cols,
+      transposed,
+      ld,
+      batch_count,
+      batch_stride);
+}
+
+void CublasGemm::run(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const Shape& batch_shape,
+    const Strides& a_batch_strides,
+    const Strides& b_batch_strides) {
+  int batch_count = out.size() / (M_ * N_);
+  if (batch_count / batch_shape.back() > 1) {
+    run_batched(
+        encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides);
+    return;
+  }
+
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+
+  execute(encoder, out.data<void>(), a.data<void>(), b.data<void>(), nullptr);
+}
+
+void CublasGemm::run(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const array& c,
+    const Shape& batch_shape,
+    const Strides& a_batch_strides,
+    const Strides& b_batch_strides,
+    const Strides& c_batch_strides,
+    float alpha,
+    float beta) {
+  int batch_count = out.size() / (M_ * N_);
+  if (batch_count / batch_shape.back() > 1) {
+    run_batched(
+        encoder,
+        out,
+        a,
+        b,
+        c,
+        batch_shape,
+        a_batch_strides,
+        b_batch_strides,
+        c_batch_strides,
+        alpha,
+        beta);
+    return;
+  }
+
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_input_array(c);
+  encoder.set_output_array(out);
+
+  execute(
+      encoder,
+      out.data<void>(),
+      a.data<void>(),
+      b.data<void>(),
+      c.data<void>(),
+      alpha,
+      beta);
+}
+
+void CublasGemm::execute(
+    cu::CommandEncoder& encoder,
+    void* out,
+    const void* a,
+    const void* b,
+    const void* c,
+    float alpha /* = 1 */,
+    float beta /* = 0 */) {
+  if (heuristic_.state != CUBLAS_STATUS_SUCCESS) {
+    int ret = 0;
+    CHECK_CUBLAS_ERROR(cublasLtMatmulAlgoGetHeuristic(
+        handle_,
+        matmul_desc_,
+        a_desc_,
+        b_desc_,
+        c ? c_desc_ : out_desc_,
+        out_desc_,
+        pref_,
+        1,
+        &heuristic_,
+        &ret));
+    if (ret == 0) {
+      throw std::runtime_error("Can not find algorithm for matmul.");
+    }
+  }
+
+  void* workspace_ptr = nullptr;
+  if (heuristic_.workspaceSize > 0) {
+    // Ensure workspace is 256-byte aligned
+    int nbytes = cuda::ceil_div(heuristic_.workspaceSize, 256) * 256;
+    array workspace(
+        allocator::malloc(nbytes),
+        {static_cast<int>(heuristic_.workspaceSize)},
+        int8);
+    encoder.add_temporary(workspace);
+    workspace_ptr = workspace.data<void>();
+  }
+
+  auto capture = encoder.capture_context();
+  CHECK_CUBLAS_ERROR(cublasLtMatmul(
+      handle_,
+      matmul_desc_,
+      &alpha,
+      a,
+      a_desc_,
+      b,
+      b_desc_,
+      &beta,
+      c ? c : out,
+      c ? c_desc_ : out_desc_,
+      out,
+      out_desc_,
+      &heuristic_.algo,
+      workspace_ptr,
+      heuristic_.workspaceSize,
+      encoder.stream()));
+}
+
+} // namespace mlx::core

mlx/backend/cuda/gemms/cublas_gemm.h

@@ -0,0 +1,124 @@
+// Copyright © 2025 Apple Inc.
+#pragma once
+
+#include "mlx/array.h"
+#include "mlx/backend/cuda/device.h"
+
+#include <cublasLt.h>
+
+namespace mlx::core {
+
+class CublasGemm {
+ public:
+  CublasGemm(
+      cu::Device& device,
+      Dtype dtype,
+      bool a_transposed,
+      uint64_t a_rows,
+      uint64_t a_cols,
+      int64_t lda,
+      bool b_transposed,
+      uint64_t b_rows,
+      uint64_t b_cols,
+      int64_t ldb,
+      int32_t batch_count,
+      int64_t a_batch_stride,
+      int64_t b_batch_stride);
+
+  CublasGemm(
+      cu::Device& device,
+      Dtype dtype,
+      bool a_transposed,
+      uint64_t a_rows,
+      uint64_t a_cols,
+      int64_t lda,
+      bool b_transposed,
+      uint64_t b_rows,
+      uint64_t b_cols,
+      int64_t ldb,
+      int64_t ldc,
+      int32_t batch_count,
+      int64_t a_batch_stride,
+      int64_t b_batch_stride,
+      int64_t c_batch_stride);
+
+  ~CublasGemm();
+
+  // The output's descriptor is inferred from inputs by default, use this method
+  // for unusual output.
+  void set_out(
+      Dtype dtype,
+      bool transposed,
+      uint64_t rows,
+      uint64_t cols,
+      int64_t ld,
+      int32_t batch_count,
+      int64_t batch_stride);
+
+  void run(
+      cu::CommandEncoder& encoder,
+      array& out,
+      const array& a,
+      const array& b,
+      const Shape& batch_shape,
+      const Strides& a_batch_strides,
+      const Strides& b_batch_strides);
+
+  void run(
+      cu::CommandEncoder& encoder,
+      array& out,
+      const array& a,
+      const array& b,
+      const array& c,
+      const Shape& batch_shape,
+      const Strides& a_batch_strides,
+      const Strides& b_batch_strides,
+      const Strides& c_batch_strides,
+      float alpha,
+      float beta);
+
+ private:
+  void run_batched(
+      cu::CommandEncoder& encoder,
+      array& out,
+      const array& a,
+      const array& b,
+      const Shape& batch_shape,
+      const Strides& a_batch_strides,
+      const Strides& b_batch_strides);
+
+  void run_batched(
+      cu::CommandEncoder& encoder,
+      array& out,
+      const array& a,
+      const array& b,
+      const array& c,
+      const Shape& batch_shape,
+      const Strides& a_batch_strides,
+      const Strides& b_batch_strides,
+      const Strides& c_batch_strides,
+      float alpha,
+      float beta);
+
+  void execute(
+      cu::CommandEncoder& encoder,
+      void* out,
+      const void* a,
+      const void* b,
+      const void* c,
+      float alpha = 1,
+      float beta = 0);
+
+  uint64_t M_;
+  uint64_t N_;
+  cublasLtMatmulPreference_t pref_{nullptr};
+  cublasLtHandle_t handle_{nullptr};
+  cublasLtMatmulDesc_t matmul_desc_{nullptr};
+  cublasLtMatrixLayout_t a_desc_{nullptr};
+  cublasLtMatrixLayout_t b_desc_{nullptr};
+  cublasLtMatrixLayout_t c_desc_{nullptr};
+  cublasLtMatrixLayout_t out_desc_{nullptr};
+  cublasLtMatmulHeuristicResult_t heuristic_;
+};
+
+} // namespace mlx::core

mlx/backend/cuda/gemms/cublas_gemm_batched_12_0.cpp

@@ -0,0 +1,73 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/common/utils.h"
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/gemms/cublas_gemm.h"
+
+namespace mlx::core {
+
+void CublasGemm::run_batched(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const Shape& batch_shape,
+    const Strides& a_batch_strides,
+    const Strides& b_batch_strides) {
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  auto nbatch = out.size() / (M_ * N_ * batch_shape.back());
+  ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
+  ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
+  auto concurrent = encoder.concurrent_context();
+  for (size_t i = 0; i < nbatch; ++i) {
+    execute(
+        encoder,
+        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M_ * N_,
+        a.data<int8_t>() + a.itemsize() * a_it.loc,
+        b.data<int8_t>() + b.itemsize() * b_it.loc,
+        nullptr);
+    a_it.step();
+    b_it.step();
+  }
+}
+
+void CublasGemm::run_batched(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const array& c,
+    const Shape& batch_shape,
+    const Strides& a_batch_strides,
+    const Strides& b_batch_strides,
+    const Strides& c_batch_strides,
+    float alpha,
+    float beta) {
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_input_array(c);
+  encoder.set_output_array(out);
+
+  auto nbatch = out.size() / (M_ * N_ * batch_shape.back());
+  ContiguousIterator a_it(batch_shape, a_batch_strides, batch_shape.size() - 1);
+  ContiguousIterator b_it(batch_shape, b_batch_strides, batch_shape.size() - 1);
+  ContiguousIterator c_it(batch_shape, c_batch_strides, batch_shape.size() - 1);
+  auto concurrent = encoder.concurrent_context();
+  for (size_t i = 0; i < nbatch; ++i) {
+    execute(
+        encoder,
+        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M_ * N_,
+        a.data<int8_t>() + a.itemsize() * a_it.loc,
+        b.data<int8_t>() + b.itemsize() * b_it.loc,
+        c.data<int8_t>() + c.itemsize() * c_it.loc,
+        alpha,
+        beta);
+    a_it.step();
+    b_it.step();
+    c_it.step();
+  }
+}
+
+} // namespace mlx::core

mlx/backend/cuda/gemms/cublas_gemm_batched_12_9.cu

@@ -0,0 +1,327 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/gemms/cublas_gemm.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+
+#include <cooperative_groups.h>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+template <int NDIM>
+__global__ void set_mm_device_pointers_nd(
+    int8_t** pointers,
+    int8_t* a_start,
+    int8_t* b_start,
+    int8_t* out_start,
+    int item_size,
+    const __grid_constant__ cuda::std::array<int32_t, NDIM> batch_shape,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_batch_strides,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_batch_strides,
+    int64_t batch_stride,
+    int batch_count) {
+  auto index = cg::this_grid().thread_rank();
+  if (index >= batch_count) {
+    return;
+  }
+  auto [a_offset, b_offset] = elem_to_loc_nd<NDIM>(
+      index,
+      batch_shape.data(),
+      a_batch_strides.data(),
+      b_batch_strides.data());
+  pointers[index] = a_start + item_size * a_offset;
+  pointers[index + batch_count] = b_start + item_size * b_offset;
+  pointers[index + 2 * batch_count] =
+      out_start + item_size * index * batch_stride;
+}
+
+__global__ void set_mm_device_pointers_g(
+    int8_t** pointers,
+    int8_t* a_start,
+    int8_t* b_start,
+    int8_t* out_start,
+    int item_size,
+    const __grid_constant__ Shape batch_shape,
+    const __grid_constant__ Strides a_batch_strides,
+    const __grid_constant__ Strides b_batch_strides,
+    int64_t batch_stride,
+    int batch_ndim,
+    int batch_count) {
+  auto index = cg::this_grid().thread_rank();
+  if (index >= batch_count) {
+    return;
+  }
+  auto [a_offset, b_offset] = elem_to_loc(
+      index,
+      batch_shape.data(),
+      a_batch_strides.data(),
+      b_batch_strides.data(),
+      batch_ndim);
+  pointers[index] = a_start + item_size * a_offset;
+  pointers[index + batch_count] = b_start + item_size * b_offset;
+  pointers[index + 2 * batch_count] =
+      out_start + item_size * index * batch_stride;
+}
+
+template <int NDIM>
+__global__ void set_addmm_device_pointers_nd(
+    int8_t** pointers,
+    int8_t* a_start,
+    int8_t* b_start,
+    int8_t* c_start,
+    int8_t* out_start,
+    int item_size,
+    const __grid_constant__ cuda::std::array<int32_t, NDIM> batch_shape,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> a_batch_strides,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> b_batch_strides,
+    const __grid_constant__ cuda::std::array<int64_t, NDIM> c_batch_strides,
+    int64_t batch_stride,
+    int batch_count) {
+  auto index = cg::this_grid().thread_rank();
+  if (index >= batch_count) {
+    return;
+  }
+  auto [a_offset, b_offset, c_offset] = elem_to_loc_nd<NDIM>(
+      index,
+      batch_shape.data(),
+      a_batch_strides.data(),
+      b_batch_strides.data(),
+      c_batch_strides.data());
+  pointers[index] = a_start + item_size * a_offset;
+  pointers[index + batch_count] = b_start + item_size * b_offset;
+  pointers[index + 2 * batch_count] = c_start + item_size * c_offset;
+  pointers[index + 3 * batch_count] =
+      out_start + item_size * index * batch_stride;
+}
+
+__global__ void set_addmm_device_pointers_g(
+    int8_t** pointers,
+    int8_t* a_start,
+    int8_t* b_start,
+    int8_t* c_start,
+    int8_t* out_start,
+    int item_size,
+    const __grid_constant__ Shape batch_shape,
+    const __grid_constant__ Strides a_batch_strides,
+    const __grid_constant__ Strides b_batch_strides,
+    const __grid_constant__ Strides c_batch_strides,
+    int64_t batch_stride,
+    int batch_ndim,
+    int batch_count) {
+  auto index = cg::this_grid().thread_rank();
+  if (index >= batch_count) {
+    return;
+  }
+  auto [a_offset, b_offset, c_offset] = elem_to_loc(
+      index,
+      batch_shape.data(),
+      a_batch_strides.data(),
+      b_batch_strides.data(),
+      c_batch_strides.data(),
+      batch_ndim);
+  pointers[index] = a_start + item_size * a_offset;
+  pointers[index + batch_count] = b_start + item_size * b_offset;
+  pointers[index + 2 * batch_count] = c_start + item_size * c_offset;
+  pointers[index + 3 * batch_count] =
+      out_start + item_size * index * batch_stride;
+}
+
+} // namespace cu
+
+namespace {
+
+void set_pointer_mode(cublasLtMatrixLayout_t desc, int batch_count) {
+  auto batch_mode = CUBLASLT_BATCH_MODE_POINTER_ARRAY;
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+      desc,
+      CUBLASLT_MATRIX_LAYOUT_BATCH_MODE,
+      &batch_mode,
+      sizeof(batch_mode)));
+  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutSetAttribute(
+      desc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch_count, sizeof(int32_t)));
+}
+
+} // namespace
+
+void CublasGemm::run_batched(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const Shape& batch_shape,
+    const Strides& a_batch_strides,
+    const Strides& b_batch_strides) {
+  int batch_count = out.size() / (M_ * N_);
+  set_pointer_mode(a_desc_, batch_count);
+  set_pointer_mode(b_desc_, batch_count);
+  set_pointer_mode(out_desc_, batch_count);
+
+  // Launch kernel to set device offsets
+  auto pointers = array(
+      allocator::malloc(batch_count * sizeof(void*) * 3),
+      {batch_count * 3},
+      uint64);
+
+  encoder.add_temporary(pointers);
+  encoder.set_output_array(pointers);
+
+  int block_dims = std::min(batch_count, 256);
+  int num_blocks = cuda::ceil_div(batch_count, block_dims);
+  int64_t batch_stride = M_ * N_;
+  int item_size = out.itemsize();
+
+  int ndim = batch_shape.size();
+  if (ndim <= 3) {
+    dispatch_1_2_3(ndim, [&](auto ndim_constant) {
+      encoder.add_kernel_node(
+          cu::set_mm_device_pointers_nd<ndim_constant()>,
+          num_blocks,
+          block_dims,
+          0,
+          pointers.data<int8_t*>(),
+          a.data<int8_t>(),
+          b.data<int8_t>(),
+          out.data<int8_t>(),
+          item_size,
+          const_param<ndim_constant()>(batch_shape),
+          const_param<ndim_constant()>(a_batch_strides),
+          const_param<ndim_constant()>(b_batch_strides),
+          batch_stride,
+          batch_count);
+    });
+  } else {
+    encoder.add_kernel_node(
+        cu::set_mm_device_pointers_g,
+        num_blocks,
+        block_dims,
+        0,
+        pointers.data<int8_t*>(),
+        a.data<int8_t>(),
+        b.data<int8_t>(),
+        out.data<int8_t>(),
+        item_size,
+        const_param(batch_shape),
+        const_param(a_batch_strides),
+        const_param(b_batch_strides),
+        batch_stride,
+        ndim,
+        batch_count);
+  }
+
+  // Run matmul
+  encoder.set_input_array(pointers);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+
+  auto a_pointers = pointers.data<int8_t*>();
+  auto b_pointers = a_pointers + batch_count;
+  auto out_pointers = b_pointers + batch_count;
+  execute(
+      encoder,
+      reinterpret_cast<void*>(out_pointers),
+      reinterpret_cast<void*>(a_pointers),
+      reinterpret_cast<void*>(b_pointers),
+      nullptr);
+}
+
+void CublasGemm::run_batched(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const array& c,
+    const Shape& batch_shape,
+    const Strides& a_batch_strides,
+    const Strides& b_batch_strides,
+    const Strides& c_batch_strides,
+    float alpha,
+    float beta) {
+  int batch_count = out.size() / (M_ * N_);
+  set_pointer_mode(a_desc_, batch_count);
+  set_pointer_mode(b_desc_, batch_count);
+  set_pointer_mode(c_desc_, batch_count);
+  set_pointer_mode(out_desc_, batch_count);
+
+  // Launch kernel to set device offsets
+  auto pointers = array(
+      allocator::malloc(batch_count * sizeof(uint64_t) * 4),
+      {batch_count * 4},
+      uint64);
+
+  encoder.add_temporary(pointers);
+  encoder.set_output_array(pointers);
+
+  int block_dims = std::min(batch_count, 256);
+  int num_blocks = cuda::ceil_div(batch_count, block_dims);
+  int64_t batch_stride = M_ * N_;
+  int item_size = out.itemsize();
+
+  int ndim = batch_shape.size();
+  if (ndim <= 3) {
+    dispatch_1_2_3(ndim, [&](auto ndim_constant) {
+      encoder.add_kernel_node(
+          cu::set_addmm_device_pointers_nd<ndim_constant()>,
+          num_blocks,
+          block_dims,
+          0,
+          pointers.data<int8_t*>(),
+          a.data<int8_t>(),
+          b.data<int8_t>(),
+          c.data<int8_t>(),
+          out.data<int8_t>(),
+          item_size,
+          const_param<ndim_constant()>(batch_shape),
+          const_param<ndim_constant()>(a_batch_strides),
+          const_param<ndim_constant()>(b_batch_strides),
+          const_param<ndim_constant()>(c_batch_strides),
+          batch_stride,
+          batch_count);
+    });
+  } else {
+    encoder.add_kernel_node(
+        cu::set_addmm_device_pointers_g,
+        num_blocks,
+        block_dims,
+        0,
+        pointers.data<int8_t*>(),
+        a.data<int8_t>(),
+        b.data<int8_t>(),
+        c.data<int8_t>(),
+        out.data<int8_t>(),
+        item_size,
+        const_param(batch_shape),
+        const_param(a_batch_strides),
+        const_param(b_batch_strides),
+        const_param(c_batch_strides),
+        batch_stride,
+        ndim,
+        batch_count);
+  }
+
+  // Run matmul
+  encoder.set_input_array(pointers);
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_input_array(c);
+  encoder.set_output_array(out);
+
+  auto a_pointers = pointers.data<int8_t*>();
+  auto b_pointers = a_pointers + batch_count;
+  auto c_pointers = b_pointers + batch_count;
+  auto out_pointers = c_pointers + batch_count;
+  execute(
+      encoder,
+      reinterpret_cast<void*>(out_pointers),
+      reinterpret_cast<void*>(a_pointers),
+      reinterpret_cast<void*>(b_pointers),
+      reinterpret_cast<void*>(c_pointers),
+      alpha,
+      beta);
+}
+
+} // namespace mlx::core

mlx/backend/cuda/gemms/cutlass_gemm.cu

@@ -0,0 +1,396 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/dtype_utils.h"
+
+#include <cute/tensor.hpp>
+#include <cutlass/arch/arch.h>
+#include <cutlass/cutlass.h>
+#include <cutlass/gemm/device/gemm.h>
+#include <cutlass/layout/matrix.h>
+#include <cutlass/numeric_types.h>
+
+#include <iostream>
+
+namespace mlx::core::cu {
+
+namespace {
+
+using namespace cute;
+using bf16 = cute::bfloat16_t;
+
+template <typename Kernel>
+void configure_matmul(Kernel kernel, int smem_size) {
+  static bool initialized = false;
+  if (!initialized) {
+    initialized = true;
+    cudaFuncSetAttribute(
+        kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size);
+  }
+}
+
+template <bool transpose, typename Tiler>
+constexpr int get_feature_size(Tiler smem) {
+  int feature_size = (transpose) ? size<0>(smem) : size<1>(smem);
+  return (feature_size >= 64) ? 64 : feature_size;
+}
+
+constexpr int constexpr_log2(int x) {
+  return (x > 0) ? 1 + constexpr_log2(x >> 1) : -1;
+}
+
+template <int feature_size, int itemsize, int copy_bits>
+constexpr int get_swizzle_bits() {
+  constexpr int swizzle_bits =
+      constexpr_log2(feature_size * itemsize / copy_bits);
+  return (swizzle_bits > 3) ? 3 : swizzle_bits;
+}
+
+template <int itemsize, bool transpose, int copy_bits, typename Tiler>
+constexpr auto make_smem_layout(Tiler smem) {
+  constexpr int feature_size = get_feature_size<transpose>(smem);
+  constexpr int swizzle_bits =
+      get_swizzle_bits<feature_size, itemsize, copy_bits>();
+
+  using F = Int<feature_size>;
+  using BaseLayout = std::conditional_t<
+      transpose,
+      Layout<cute::Shape<F, _8>, cute::Stride<_1, F>>,
+      Layout<cute::Shape<_8, F>, cute::Stride<F, _1>>>;
+
+  auto swizzled =
+      make_composed_layout(Swizzle<swizzle_bits, 3, 3>{}, 0, BaseLayout{});
+
+  return tile_to_shape(swizzled, smem);
+}
+
+template <int itemsize, bool transpose, int copy_bits, typename Tiler>
+constexpr auto make_result_smem_layout(Tiler smem) {
+  constexpr int feature_size = get_feature_size<transpose>(smem);
+  constexpr int swizzle_bits =
+      get_swizzle_bits<feature_size, itemsize, copy_bits>();
+
+  using F = Int<feature_size>;
+  using BaseLayout = std::conditional_t<
+      transpose,
+      Layout<cute::Shape<F, _8>, cute::Stride<_1, F>>,
+      Layout<cute::Shape<_8, F>, cute::Stride<F, _1>>>;
+
+  auto swizzled = make_composed_layout(
+      Swizzle<transpose ? 0 : swizzle_bits, 3, 4>{}, 0, BaseLayout{});
+
+  return tile_to_shape(swizzled, smem);
+}
+
+template <
+    int num_threads,
+    int itemsize,
+    bool transpose,
+    int copy_bits,
+    typename Copier,
+    typename Tiler>
+constexpr auto make_tiled_copy(Copier copy_op, Tiler smem) {
+  constexpr int num_elements = copy_bits / itemsize;
+  constexpr int feature_size = transpose ? size<0>(smem) : size<1>(smem);
+  constexpr int copies_per_feature = feature_size / num_elements;
+
+  using E = Int<num_elements>;
+  using C = Int<copies_per_feature>;
+  using R = Int<num_threads / copies_per_feature>;
+
+  using ThreadLayout = std::conditional_t<
+      transpose,
+      Layout<cute::Shape<C, R>, cute::Stride<_1, C>>,
+      Layout<cute::Shape<R, C>, cute::Stride<C, _1>>>;
+  using ValueLayout = std::conditional_t<
+      transpose,
+      Layout<cute::Shape<E, _1>>,
+      Layout<cute::Shape<_1, E>>>;
+
+  return make_tiled_copy(copy_op, ThreadLayout{}, ValueLayout{});
+}
+
+template <int rasterization_factor>
+__device__ inline int2 raster_tile(int x, int y) {
+  return {
+      x / rasterization_factor,
+      (x % rasterization_factor) + y * rasterization_factor};
+}
+
+template <
+    typename T,
+    typename SLayoutA,
+    typename SLayoutB,
+    typename SLayoutC,
+    typename CopyA,
+    typename CopyB,
+    typename CopyC,
+    typename MMA,
+    int rasterization_factor>
+__global__ static __launch_bounds__(decltype(size(MMA{}))::value) void matmul_kernel(
+    const T* __restrict__ A,
+    const T* __restrict__ B,
+    T* __restrict__ C,
+    SLayoutA SA,
+    SLayoutB SB,
+    SLayoutC SC,
+    CopyA copy_a,
+    CopyB copy_b,
+    CopyC copy_c,
+    MMA mma,
+    int M,
+    int N,
+    int K) {
+  constexpr auto BM = size<0>(SA);
+  constexpr auto BN = size<0>(SB);
+  constexpr auto BK = size<1>(SA);
+  constexpr auto PIPE = size<2>(SA);
+
+  const int2 tile = raster_tile<rasterization_factor>(blockIdx.x, blockIdx.y);
+  const int blocks_m = ceil_div(M, BM);
+  const int blocks_n = ceil_div(N, BN);
+
+  // Exit early if the tile is OOB
+  if (tile.x >= blocks_m || tile.y >= blocks_n) {
+    return;
+  }
+
+  // Make the full tensors
+  Tensor full_A =
+      make_tensor(make_gmem_ptr(A), make_shape(M, K), make_stride(K, _1{}));
+  Tensor full_B =
+      make_tensor(make_gmem_ptr(B), make_shape(N, K), make_stride(K, _1{}));
+  Tensor full_C =
+      make_tensor(make_gmem_ptr(C), make_shape(M, N), make_stride(N, _1{}));
+
+  // Partition the tensors into tiles and select the ones for this threadblock
+  Tensor local_A =
+      local_tile(full_A, make_shape(BM, BK), make_coord(tile.x, _));
+  Tensor local_B =
+      local_tile(full_B, make_shape(BN, BK), make_coord(tile.y, _));
+  Tensor local_C =
+      local_tile(full_C, make_shape(BM, BN), make_coord(tile.x, tile.y));
+
+  // Make shared memory tensors
+  extern __shared__ char shared_memory[];
+  T* shared_A_ptr = reinterpret_cast<T*>(shared_memory);
+  T* shared_B_ptr =
+      reinterpret_cast<T*>(shared_memory + cosize(SA) * sizeof(T));
+  T* shared_C_ptr = reinterpret_cast<T*>(shared_memory);
+  Tensor shared_A = make_tensor(make_smem_ptr(shared_A_ptr), SA);
+  Tensor shared_B = make_tensor(make_smem_ptr(shared_B_ptr), SB);
+  Tensor shared_C = make_tensor(make_smem_ptr(shared_C_ptr), SC);
+
+  // Get the copies that correspond to this thread
+  auto thread_copy_a = copy_a.get_slice(threadIdx.x);
+  Tensor local_A_src = thread_copy_a.partition_S(local_A);
+  Tensor local_A_dst = thread_copy_a.partition_D(shared_A);
+  auto thread_copy_b = copy_b.get_slice(threadIdx.x);
+  Tensor local_B_src = thread_copy_a.partition_S(local_B);
+  Tensor local_B_dst = thread_copy_a.partition_D(shared_B);
+  auto thread_copy_c = copy_c.get_slice(threadIdx.x);
+  Tensor local_C_src = thread_copy_c.partition_S(shared_C);
+  Tensor local_C_dst = thread_copy_c.partition_D(local_C);
+
+  // Start fetches
+  int k_tile_count = size<2>(local_A);
+  int k_tile_next = 0;
+  CUTE_UNROLL
+  for (int k = 0; k < PIPE - 1; k++) {
+    copy(copy_a, local_A_src(_, _, _, k_tile_next), local_A_dst(_, _, _, k));
+    copy(copy_b, local_B_src(_, _, _, k_tile_next), local_B_dst(_, _, _, k));
+    cp_async_fence();
+    k_tile_count--;
+    k_tile_next += (k_tile_count > 0);
+  }
+
+  // Get the MMA that corresponds to this thread and allocate registers
+  auto thread_mma = mma.get_slice(threadIdx.x);
+  Tensor mma_shared_A = thread_mma.partition_A(shared_A);
+  Tensor mma_shared_B = thread_mma.partition_B(shared_B);
+  Tensor mma_shared_C = thread_mma.partition_C(shared_C);
+  Tensor mma_global_C = thread_mma.partition_C(local_C);
+  Tensor mma_frag_A = mma.make_fragment_A(mma_shared_A(_, _, _, 0));
+  Tensor mma_frag_B = mma.make_fragment_B(mma_shared_B(_, _, _, 0));
+  Tensor mma_frag_C = mma.make_fragment_C(mma_global_C);
+  clear(mma_frag_C);
+
+  // Make shared to register copies
+  Copy_Atom<SM75_U32x4_LDSM_N, bf16> s2r_atom_a;
+  Copy_Atom<SM75_U32x4_LDSM_N, bf16> s2r_atom_b;
+  auto s2r_copy_a = make_tiled_copy_A(s2r_atom_a, mma);
+  auto s2r_thread_copy_a = s2r_copy_a.get_slice(threadIdx.x);
+  auto s2r_copy_b = make_tiled_copy_B(s2r_atom_b, mma);
+  auto s2r_thread_copy_b = s2r_copy_b.get_slice(threadIdx.x);
+  Tensor mma_A_src = s2r_thread_copy_a.partition_S(shared_A);
+  Tensor mma_A_dst = s2r_thread_copy_a.retile_D(mma_frag_A);
+  Tensor mma_B_src = s2r_thread_copy_b.partition_S(shared_B);
+  Tensor mma_B_dst = s2r_thread_copy_b.retile_D(mma_frag_B);
+
+  constexpr auto RPIPE = size<2>(mma_shared_A);
+  int smem_read = 0;
+  int smem_write = PIPE - 1;
+  Tensor mma_A_src_p = mma_A_src(_, _, _, smem_read);
+  Tensor mma_B_src_p = mma_B_src(_, _, _, smem_read);
+
+  // Start the register pipeline
+  if constexpr (RPIPE > 1) {
+    cp_async_wait<PIPE - 2>();
+    __syncthreads();
+    copy(s2r_copy_a, mma_A_src_p(_, _, Int<0>{}), mma_A_dst(_, _, Int<0>{}));
+    copy(s2r_copy_b, mma_B_src_p(_, _, Int<0>{}), mma_B_dst(_, _, Int<0>{}));
+  }
+
+  CUTE_NO_UNROLL
+  while (k_tile_count > -(PIPE - 1)) {
+    CUTE_UNROLL
+    for (int k_block = 0; k_block < RPIPE; k_block++) {
+      if (k_block == RPIPE - 1) {
+        mma_A_src_p = mma_A_src(_, _, _, smem_read);
+        mma_B_src_p = mma_B_src(_, _, _, smem_read);
+        cp_async_wait<PIPE - 2>();
+        __syncthreads();
+      }
+
+      // Load the next register tile
+      auto k_block_next = (k_block + 1) % RPIPE;
+      copy(
+          s2r_copy_a,
+          mma_A_src_p(_, _, k_block_next),
+          mma_A_dst(_, _, k_block_next));
+      copy(
+          s2r_copy_b,
+          mma_B_src_p(_, _, k_block_next),
+          mma_B_dst(_, _, k_block_next));
+
+      if (k_block == 0) {
+        copy(
+            copy_a,
+            local_A_src(_, _, _, k_tile_next),
+            local_A_dst(_, _, _, smem_write));
+        copy(
+            copy_b,
+            local_B_src(_, _, _, k_tile_next),
+            local_B_dst(_, _, _, smem_write));
+        cp_async_fence();
+        k_tile_count--;
+        k_tile_next += (k_tile_count > 0);
+        smem_write = smem_read;
+        smem_read = (smem_read == PIPE - 1) ? 0 : (smem_read + 1);
+      }
+
+      gemm(
+          mma,
+          mma_frag_A(_, _, k_block),
+          mma_frag_B(_, _, k_block),
+          mma_frag_C);
+    }
+  }
+
+  copy(mma_frag_C, mma_shared_C);
+  __syncthreads();
+  copy(copy_c, local_C_src, local_C_dst);
+
+  // if (threadIdx.x == 0) {
+  //   print("fC: "); print(mma_frag_C); print("\n");
+  //   print("sC: "); print(mma_shared_C); print("\n");
+  //   print("dC: "); print(local_C_dst); print("\n");
+  //
+  //   print(s2r_atom_a); print("\n");
+  // }
+}
+
+} // namespace
+
+void cutlass_gemm(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    cu::CommandEncoder& enc) {
+  enc.set_input_array(a);
+  enc.set_input_array(b);
+  enc.set_output_array(out);
+  dispatch_float_types(a.dtype(), "simple_gemm", [&](auto type_tag) {
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    if constexpr (std::is_same_v<DataType, __nv_bfloat16>) {
+      using namespace cute;
+
+      // Tile definitions
+      auto BM = Int<128>{};
+      auto BN = Int<128>{};
+      auto BK = Int<64>{};
+      auto BP = Int<3>{};
+      auto GM = Int<8>{};
+
+      // Thread definitions
+      using TM = Int<2>;
+      using TN = Int<2>;
+      using TK = Int<1>;
+      constexpr int num_threads = TM::value * TN::value * 32;
+
+      auto SA = make_smem_layout<16, false, 128>(make_shape(BM, BK, BP));
+      auto SB = make_smem_layout<16, false, 128>(make_shape(BN, BK, BP));
+      auto SC = make_result_smem_layout<16, false, 128>(make_shape(BM, BN));
+
+      constexpr auto smem_size = (cosize(SA) + cosize(SB)) * sizeof(bf16);
+
+      auto async_copy_op =
+          Copy_Atom<SM80_CP_ASYNC_CACHEALWAYS<uint128_t>, bf16>{};
+      auto tiled_copy_a = make_tiled_copy<num_threads, 16, false, 128>(
+          async_copy_op, make_shape(BM, BK));
+      auto tiled_copy_b = make_tiled_copy<num_threads, 16, false, 128>(
+          async_copy_op, make_shape(BN, BK));
+
+      auto sync_copy_op = Copy_Atom<UniversalCopy<uint128_t>, bf16>{};
+      auto tiled_copy_c = make_tiled_copy<num_threads, 16, false, 128>(
+          sync_copy_op, make_shape(BM, BN));
+
+      auto mma_op = SM80_16x8x16_F32BF16BF16F32_TN{};
+      auto tiled_mma = make_tiled_mma(
+          mma_op, Layout<cute::Shape<TM, TN, TK>>{}, Tile<_32, _32, _16>{});
+
+      auto kernel = matmul_kernel<
+          bf16,
+          decltype(SA),
+          decltype(SB),
+          decltype(SC),
+          decltype(tiled_copy_a),
+          decltype(tiled_copy_b),
+          decltype(tiled_copy_c),
+          decltype(tiled_mma),
+          GM.value>;
+      configure_matmul(kernel, smem_size);
+
+      dim3 block(size(tiled_mma));
+      dim3 grid(
+          size(ceil_div(M, BM) * GM), size(ceil_div(ceil_div(N, BN), GM)));
+
+      enc.add_kernel_node(
+          kernel,
+          grid,
+          block,
+          smem_size,
+          a.data<bf16>(),
+          b.data<bf16>(),
+          out.data<bf16>(),
+          SA,
+          SB,
+          SC,
+          tiled_copy_a,
+          tiled_copy_b,
+          tiled_copy_c,
+          tiled_mma,
+          M,
+          N,
+          K);
+    } else {
+      throw std::runtime_error("Only bfloat16 supported");
+    }
+  });
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/cutlass_gemm.h

@@ -0,0 +1,18 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/device.h"
+
+namespace mlx::core::cu {
+
+void cutlass_gemm(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    cu::CommandEncoder& enc);
+
+}

mlx/backend/cuda/gemms/gemv.cu

@@ -0,0 +1,173 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/gemms/gemv.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/dtype_utils.h"
+
+#include <cooperative_groups.h>
+#include <cooperative_groups/reduce.h>
+
+namespace mlx::core::cu {
+
+namespace cg = cooperative_groups;
+
+static constexpr int rows_per_block = 8;
+
+template <typename T, int rows_per_block, int n_per_thread>
+__device__ void
+gemv_impl(const T* mat, const T* vec, T* out, int rows, int cols) {
+  auto block = cg::this_thread_block();
+  auto warp = cg::tiled_partition<WARP_SIZE>(block);
+
+  auto g_idx = block.group_index();
+  auto t_idx = block.thread_index();
+  int row = g_idx.x * rows_per_block + t_idx.y;
+
+  if (row < rows) {
+    float sum = 0.0f;
+    for (int col = n_per_thread * warp.thread_rank(); col < cols;
+         col += (WARP_SIZE * n_per_thread)) {
+      auto local_mat =
+          unsafe_load_vector<n_per_thread>(mat + row * cols + col, 0);
+      auto local_vec = unsafe_load_vector<n_per_thread>(vec + col, 0);
+#pragma unroll
+      for (int j = 0; j < n_per_thread; ++j) {
+        sum +=
+            static_cast<float>(local_mat[j]) * static_cast<float>(local_vec[j]);
+      }
+    }
+
+    sum = cg::reduce(warp, sum, cg::plus<float>{});
+    if (warp.thread_rank() == 0) {
+      out[row] = static_cast<T>(sum);
+    }
+  }
+}
+
+template <typename T, int rows_per_block, int n_per_thread>
+__global__ void
+gemv_single(const T* mat, const T* vec, T* out, int rows, int cols) {
+  gemv_impl<T, rows_per_block, n_per_thread>(mat, vec, out, rows, cols);
+}
+
+template <typename T, int rows_per_block, int n_per_thread>
+__global__ void gemv_batched(
+    const T* mat,
+    const T* vec,
+    T* out,
+    int rows,
+    int cols,
+    const __grid_constant__ Shape batch_shape,
+    const __grid_constant__ Strides mat_batch_strides,
+    const __grid_constant__ Strides vec_batch_strides,
+    int batch_ndim) {
+  auto block = cg::this_thread_block();
+  auto batch_idx = block.group_index().y;
+  auto [vec_offset, mat_offset] = elem_to_loc(
+      batch_idx,
+      batch_shape.data(),
+      vec_batch_strides.data(),
+      mat_batch_strides.data(),
+      batch_ndim);
+  gemv_impl<T, rows_per_block, n_per_thread>(
+      mat + mat_offset, vec + vec_offset, out + batch_idx * rows, rows, cols);
+}
+
+bool can_use_gemv(int M, int N, int K, bool a_transposed, bool b_transposed) {
+  return K % 32 == 0 && ((M == 1 && b_transposed) || (N == 1 && !a_transposed));
+}
+
+template <typename F>
+void dispatch_n_per_thread(int n_per_thread, F&& f) {
+  switch (n_per_thread) {
+    case 1:
+      f(std::integral_constant<int, 1>{});
+      break;
+    case 2:
+      f(std::integral_constant<int, 2>{});
+      break;
+    case 4:
+      f(std::integral_constant<int, 4>{});
+      break;
+  }
+}
+
+void gemv(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    uint32_t batch_count,
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides,
+    CommandEncoder& encoder) {
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  encoder.set_output_array(out);
+  dispatch_float_types(out.dtype(), "gemv", [&](auto type_tag) {
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    dim3 block_dims{WARP_SIZE, rows_per_block};
+    const DataType* mat;
+    const DataType* vec;
+    int rows;
+    int cols = K;
+    auto mat_strides = const_param(a_batch_strides);
+    auto vec_strides = const_param(b_batch_strides);
+
+    if (M == 1) {
+      mat = b.data<DataType>();
+      vec = a.data<DataType>();
+      rows = N;
+      std::swap(mat_strides, vec_strides);
+    } else {
+      mat = a.data<DataType>();
+      vec = b.data<DataType>();
+      rows = M;
+    }
+    uint32_t num_blocks_x = (rows + rows_per_block - 1) / rows_per_block;
+    int n_per_t;
+    if (K % 128 == 0 && is_aligned<4>(mat) && is_aligned<4>(vec)) {
+      n_per_t = 4;
+    } else if (K % 64 == 0 && is_aligned<2>(mat) && is_aligned<2>(vec)) {
+      n_per_t = 2;
+    } else {
+      n_per_t = 1;
+    }
+    dispatch_n_per_thread(n_per_t, [&](auto n_per_thread) {
+      if (batch_count == 1) {
+        auto kernel = gemv_single<DataType, rows_per_block, n_per_thread()>;
+        encoder.add_kernel_node(
+            kernel,
+            num_blocks_x,
+            block_dims,
+            0,
+            mat,
+            vec,
+            out.data<DataType>(),
+            rows,
+            cols);
+      } else {
+        auto kernel = gemv_batched<DataType, rows_per_block, n_per_thread()>;
+        encoder.add_kernel_node(
+            kernel,
+            dim3{num_blocks_x, batch_count},
+            block_dims,
+            0,
+            mat,
+            vec,
+            out.data<DataType>(),
+            rows,
+            cols,
+            const_param(batch_shape),
+            mat_strides,
+            vec_strides,
+            batch_shape.size());
+      }
+    });
+  });
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/gemv.h

@@ -0,0 +1,24 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/device.h"
+
+namespace mlx::core::cu {
+
+bool can_use_gemv(int M, int N, int K, bool a_transposed, bool b_transposed);
+
+void gemv(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    uint32_t batch_count,
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides,
+    CommandEncoder& encoder);
+
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/simple_gemm.cu

@@ -0,0 +1,69 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/steel/gemm.cuh"
+#include "mlx/dtype_utils.h"
+
+#include <iostream>
+
+namespace mlx::core::cu {
+
+namespace {
+
+template <typename Kernel>
+static void configure_smem(Kernel kernel, int SM) {
+  static bool done = false;
+  if (done) {
+    return;
+  }
+  std::cout << "configuring" << std::endl;
+  cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, SM);
+  cudaFuncSetAttribute(
+      kernel,
+      cudaFuncAttributePreferredSharedMemoryCarveout,
+      cudaSharedmemCarveoutMaxShared);
+  done = true;
+}
+
+} // namespace
+
+void simple_gemm(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    cu::CommandEncoder& enc) {
+  enc.set_input_array(a);
+  enc.set_input_array(b);
+  enc.set_output_array(out);
+  dispatch_float_types(a.dtype(), "simple_gemm", [&](auto type_tag) {
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    constexpr int BM = 128;
+    constexpr int BN = 128;
+    constexpr int BK = 32;
+    constexpr int PIPE = 3;
+    constexpr int SM = PIPE * sizeof(DataType) * (BM * BK + BN * BK);
+    constexpr int WM = 2;
+    constexpr int WN = 4;
+
+    auto kernel = ab_t_aligned<DataType, BM, BN, BK, WM, WN, PIPE>;
+    configure_smem(kernel, SM);
+
+    dim3 grid(N / BN, M / BM);
+    enc.add_kernel_node(
+        kernel,
+        grid,
+        WM * WN * WARP_SIZE,
+        SM,
+        a.data<DataType>(),
+        b.data<DataType>(),
+        out.data<DataType>(),
+        N,
+        K);
+  });
+}
+
+} // namespace mlx::core::cu

mlx/backend/cuda/gemms/simple_gemm.h

@@ -0,0 +1,18 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include "mlx/backend/cuda/device.h"
+
+namespace mlx::core::cu {
+
+void simple_gemm(
+    const array& a,
+    const array& b,
+    array& out,
+    int M,
+    int N,
+    int K,
+    cu::CommandEncoder& enc);
+
+}

mlx/backend/cuda/indexing.cpp

@@ -29,12 +29,12 @@ void append_indices_arg(
     const std::vector<array>& inputs,
     int nidx,
     int idx_ndim) {
-  std::vector<const void*> indices(nidx);
+  SmallVector<const void*> indices(nidx);
   for (int i = 0; i < nidx; ++i) {
     indices[i] = inputs[i + 1].data<void>();
   }
   args.append(std::move(indices));
-  std::vector<int32_t> indices_shape(nidx * idx_ndim);
+  SmallVector<int32_t> indices_shape(nidx * idx_ndim);
   for (int i = 0; i < nidx; ++i) {
     std::copy_n(
         inputs[i + 1].shape().begin(),
@@ -42,7 +42,7 @@ void append_indices_arg(
         indices_shape.data() + i * idx_ndim);
   }
   args.append(std::move(indices_shape));
-  std::vector<int64_t> indices_strides(nidx * idx_ndim);
+  SmallVector<int64_t> indices_strides(nidx * idx_ndim);
   for (int i = 0; i < nidx; ++i) {
     std::copy_n(
         inputs[i + 1].strides().begin(),
@@ -94,7 +94,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
             large ? "int64_t" : "int32_t"));
       }
     }
-    return std::make_pair(jit_source_gather, std::move(kernel_names));
+    return std::make_tuple(false, jit_source_gather, std::move(kernel_names));
   });
 
   cu::KernelArgs args;
@@ -110,7 +110,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   args.append<int32_t>(src.ndim());
   args.append_ndim(slice_sizes_);
   args.append(slice_size);
-  args.append(axes_);
+  args.append(SmallVector<int32_t>(axes_.begin(), axes_.end()));
   append_indices_arg(args, inputs, nidx, idx_ndim);
 
   std::string kernel_name = fmt::format(
@@ -128,8 +128,8 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {
   encoder.set_output_array(out);
 
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, out, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
+  auto [num_blocks, block_dims] = get_launch_args(out, large);
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
 }
 
 void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -189,7 +189,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
             large ? "int64_t" : "int32_t"));
       }
     }
-    return std::make_pair(jit_source_scatter, std::move(kernel_names));
+    return std::make_tuple(false, jit_source_scatter, std::move(kernel_names));
   });
 
   cu::KernelArgs args;
@@ -211,7 +211,7 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   args.append_ndim(out.shape());
   args.append_ndim(out.strides());
   args.append<int32_t>(out.ndim());
-  args.append(axes_);
+  args.append(SmallVector<int32_t>(axes_.begin(), axes_.end()));
   append_indices_arg(args, inputs, nidx, idx_ndim);
 
   std::string kernel_name = fmt::format(
@@ -229,8 +229,8 @@ void Scatter::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
   encoder.set_output_array(out);
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, upd, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
+  auto [num_blocks, block_dims] = get_launch_args(upd, large);
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
 }
 
 void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -268,7 +268,8 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
         }
       }
     }
-    return std::make_pair(jit_source_gather_axis, std::move(kernel_names));
+    return std::make_tuple(
+        false, jit_source_gather_axis, std::move(kernel_names));
   });
 
   size_t idx_size_pre = 1;
@@ -317,8 +318,8 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
   encoder.set_output_array(out);
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, idx, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
+  auto [num_blocks, block_dims] = get_launch_args(idx, large);
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
 }
 
 void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -371,7 +372,8 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
         }
       }
     }
-    return std::make_pair(jit_source_scatter_axis, std::move(kernel_names));
+    return std::make_tuple(
+        false, jit_source_scatter_axis, std::move(kernel_names));
   });
 
   size_t idx_size_pre = 1;
@@ -421,8 +423,8 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
   }
   encoder.set_output_array(out);
   auto kernel = mod.get_kernel(kernel_name);
-  auto [num_blocks, block_dims] = get_launch_args(kernel, idx, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
+  auto [num_blocks, block_dims] = get_launch_args(idx, large);
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
 }
 
 } // namespace mlx::core

mlx/backend/cuda/iterators/general_iterator.cuh

@@ -1,121 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include <thrust/iterator/iterator_adaptor.h>
-#include <cuda/std/utility>
-
-#include "mlx/backend/cuda/kernel_utils.cuh"
-
-namespace mlx::core::cu {
-
-// Iterating non-contiguous array.
-template <typename Iterator, typename IdxT = int64_t>
-class general_iterator
-    : public thrust::
-          iterator_adaptor<general_iterator<Iterator, IdxT>, Iterator> {
- public:
-  using super_t =
-      thrust::iterator_adaptor<general_iterator<Iterator, IdxT>, Iterator>;
-
-  using reference = typename super_t::reference;
-  using difference_type = typename super_t::difference_type;
-
-  __host__ __device__ general_iterator(
-      Iterator it,
-      IdxT index,
-      int ndim,
-      Shape shape,
-      Strides strides)
-      : super_t(it),
-        index_(index),
-        ndim_(ndim),
-        shape_(cuda::std::move(shape)),
-        strides_(cuda::std::move(strides)) {}
-
-  __host__ __device__ IdxT index() const {
-    return index_;
-  }
-
-  __host__ __device__ const Shape& shape() const {
-    return shape_;
-  }
-
-  __host__ __device__ const Strides& strides() const {
-    return strides_;
-  }
-
- private:
-  friend class thrust::iterator_core_access;
-
-  __host__ __device__ bool equal(const general_iterator& other) const {
-    return this->base() == other.base() && this->index() == other.index();
-  }
-
-  __host__ __device__ void advance(difference_type n) {
-    this->index_ += n;
-  }
-
-  __host__ __device__ void increment() {
-    this->index_ += 1;
-  }
-
-  __host__ __device__ void decrement() {
-    this->index_ -= 1;
-  }
-
-  __host__ __device__ difference_type
-  distance_to(const general_iterator& other) const {
-    _CCCL_ASSERT(
-        this->base() == other.base(),
-        "Underlying iterator must point to same base iterator");
-    return other.index() - this->index();
-  }
-
-  // The dereference is device-only to avoid accidental running in host.
-  __device__ typename super_t::reference dereference() const {
-    IdxT offset = elem_to_loc(index_, shape_.data(), strides_.data(), ndim_);
-    return *(this->base() + offset);
-  }
-
-  IdxT index_;
-  int ndim_;
-  Shape shape_;
-  Strides strides_;
-};
-
-template <typename IdxT, typename Iterator>
-__host__ __device__ auto make_general_iterator(
-    Iterator it,
-    IdxT index,
-    int ndim,
-    Shape shape,
-    Strides strides) {
-  return general_iterator<Iterator, IdxT>(
-      it, index, ndim, cuda::std::move(shape), cuda::std::move(strides));
-}
-
-template <typename IdxT, typename Iterator>
-auto make_general_iterator(
-    Iterator it,
-    const std::vector<int32_t>& shape,
-    const std::vector<int64_t>& strides) {
-  return make_general_iterator<IdxT>(
-      it, 0, shape.size(), const_param(shape), const_param(strides));
-}
-
-template <typename IdxT, typename Iterator>
-auto make_general_iterators(
-    Iterator it,
-    IdxT size,
-    const std::vector<int32_t>& shape,
-    const std::vector<int64_t>& strides) {
-  auto ndim = shape.size();
-  auto shape_arg = const_param(shape);
-  auto strides_arg = const_param(strides);
-  return std::make_pair(
-      make_general_iterator<IdxT>(it, 0, ndim, shape_arg, strides_arg),
-      make_general_iterator<IdxT>(it, size, ndim, shape_arg, strides_arg));
-}
-
-} // namespace mlx::core::cu

mlx/backend/cuda/iterators/strided_iterator.cuh

@@ -1,60 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#pragma once
-
-#include <thrust/iterator/iterator_adaptor.h>
-#include <thrust/iterator/iterator_facade.h>
-
-namespace mlx::core::cu {
-
-// RandomAccessIterator for strided access to array entries.
-template <typename Iterator, typename Stride = int64_t>
-class strided_iterator
-    : public thrust::
-          iterator_adaptor<strided_iterator<Iterator, Stride>, Iterator> {
- public:
-  using super_t =
-      thrust::iterator_adaptor<strided_iterator<Iterator, Stride>, Iterator>;
-
-  using reference = typename super_t::reference;
-  using difference_type = typename super_t::difference_type;
-
-  __host__ __device__ strided_iterator(Iterator it, Stride stride)
-      : super_t(it), stride_(stride) {}
-
-  __host__ __device__ Stride stride() const {
-    return stride_;
-  }
-
- private:
-  friend class thrust::iterator_core_access;
-
-  __host__ __device__ bool equal(const strided_iterator& other) const {
-    return this->base() == other.base();
-  }
-
-  __host__ __device__ void advance(difference_type n) {
-    this->base_reference() += n * stride_;
-  }
-
-  __host__ __device__ void increment() {
-    this->base_reference() += stride_;
-  }
-
-  __host__ __device__ void decrement() {
-    this->base_reference() -= stride_;
-  }
-
-  __host__ __device__ difference_type
-  distance_to(const strided_iterator& other) const {
-    const difference_type dist = other.base() - this->base();
-    _CCCL_ASSERT(
-        dist % stride() == 0,
-        "Underlying iterator difference must be divisible by the stride");
-    return dist / stride();
-  }
-
-  Stride stride_;
-};
-
-} // namespace mlx::core::cu

mlx/backend/cuda/jit_module.cpp

@@ -9,7 +9,6 @@
 #include <cstdlib>
 #include <filesystem>
 #include <fstream>
-#include <unordered_map>
 
 #include <fmt/format.h>
 #include <nvrtc.h>
@@ -102,8 +101,8 @@ const std::filesystem::path& ptx_cache_dir() {
 bool read_cached_ptx(
     const std::filesystem::path& cache_dir,
     const std::string& module_name,
-    std::vector<char>* ptx,
-    std::vector<std::pair<std::string, std::string>>* ptx_kernels) {
+    std::string& ptx,
+    std::vector<std::pair<std::string, std::string>>& ptx_kernels) {
   if (cache_dir.empty()) {
     return false;
   }
@@ -118,15 +117,15 @@ bool read_cached_ptx(
   if (!ptx_file.good()) {
     return false;
   }
-  ptx->resize(ptx_size);
-  ptx_file.read(ptx->data(), ptx_size);
+  ptx.resize(ptx_size);
+  ptx_file.read(ptx.data(), ptx_size);
 
   std::ifstream txt_file(cache_dir / (module_name + ".txt"), std::ios::binary);
   std::string line;
   while (std::getline(txt_file, line)) {
     auto tab = line.find('\t');
     if (tab != std::string::npos) {
-      ptx_kernels->emplace_back(line.substr(0, tab), line.substr(tab + 1));
+      ptx_kernels.emplace_back(line.substr(0, tab), line.substr(tab + 1));
     }
   }
   return true;
@@ -136,7 +135,7 @@ bool read_cached_ptx(
 void write_cached_ptx(
     const std::filesystem::path& cache_dir,
     const std::string& module_name,
-    const std::vector<char>& ptx,
+    const std::string& ptx,
     const std::vector<std::pair<std::string, std::string>>& ptx_kernels,
     const std::string& source_code) {
   if (cache_dir.empty()) {
@@ -218,85 +217,85 @@ constexpr const char* g_headers[] = {
     jit_source_utils,
 };
 
-} // namespace
-
-JitModule::JitModule(
+void compile(
     Device& device,
     const std::string& module_name,
-    const KernelBuilder& builder) {
-  // Check cache.
-  std::vector<char> ptx;
-  std::vector<std::pair<std::string, std::string>> ptx_kernels;
-  if (!read_cached_ptx(ptx_cache_dir(), module_name, &ptx, &ptx_kernels)) {
-    // Create program.
-    auto [source_code, kernel_names] = builder();
-    nvrtcProgram prog;
-    CHECK_NVRTC_ERROR(nvrtcCreateProgram(
-        &prog,
-        source_code.c_str(),
-        (module_name + ".cu").c_str(),
-        std::size(g_headers),
-        g_headers,
-        g_include_names));
-    std::unique_ptr<nvrtcProgram, void (*)(nvrtcProgram*)> prog_freer(
-        &prog,
-        [](nvrtcProgram* p) { CHECK_NVRTC_ERROR(nvrtcDestroyProgram(p)); });
-    for (const auto& name : kernel_names) {
-      CHECK_NVRTC_ERROR(nvrtcAddNameExpression(prog, name.c_str()));
-    }
-
-    // Compile program.
-    std::vector<const char*> args;
-    bool use_sass = compiler_supports_device_sass(device);
-    std::string compute = fmt::format(
-        "--gpu-architecture={}_{}{}",
-        use_sass ? "sm" : "compute",
-        device.compute_capability_major(),
-        device.compute_capability_minor());
-    args.push_back(compute.c_str());
-    std::string cccl_include = cccl_dir();
-    if (!cccl_include.empty()) {
-      cccl_include = fmt::format("--include-path={}", cccl_include);
-      args.push_back(cccl_include.c_str());
-    }
-    std::string cuda_include =
-        fmt::format("--include-path={}/include", cuda_home());
-    args.push_back(cuda_include.c_str());
-    nvrtcResult compile_result =
-        nvrtcCompileProgram(prog, args.size(), args.data());
-    if (compile_result != NVRTC_SUCCESS) {
-      size_t log_size;
-      CHECK_NVRTC_ERROR(nvrtcGetProgramLogSize(prog, &log_size));
-      std::vector<char> log(log_size + 1, 0);
-      CHECK_NVRTC_ERROR(nvrtcGetProgramLog(prog, log.data()));
-      throw std::runtime_error(
-          fmt::format("Failed to compile kernel: {}.", log.data()));
-    }
-
-    // Get mangled names of kernel names.
-    for (const auto& name : kernel_names) {
-      const char* mangled;
-      CHECK_NVRTC_ERROR(nvrtcGetLoweredName(prog, name.c_str(), &mangled));
-      ptx_kernels.emplace_back(name, mangled);
-    }
-
-    // Get ptx data.
-    size_t ptx_size;
-    if (use_sass) {
-      CHECK_NVRTC_ERROR(nvrtcGetCUBINSize(prog, &ptx_size));
-    } else {
-      CHECK_NVRTC_ERROR(nvrtcGetPTXSize(prog, &ptx_size));
-    }
-    ptx.resize(ptx_size, 0);
-    if (use_sass) {
-      CHECK_NVRTC_ERROR(nvrtcGetCUBIN(prog, ptx.data()));
-    } else {
-      CHECK_NVRTC_ERROR(nvrtcGetPTX(prog, ptx.data()));
-    }
-    write_cached_ptx(
-        ptx_cache_dir(), module_name, ptx, ptx_kernels, source_code);
+    const std::string& source,
+    const std::vector<std::string>& kernel_names,
+    std::string& ptx,
+    std::vector<std::pair<std::string, std::string>>& ptx_kernels) {
+  // Create the program
+  nvrtcProgram prog;
+  CHECK_NVRTC_ERROR(nvrtcCreateProgram(
+      &prog,
+      source.c_str(),
+      (module_name + ".cu").c_str(),
+      std::size(g_headers),
+      g_headers,
+      g_include_names));
+  std::unique_ptr<nvrtcProgram, void (*)(nvrtcProgram*)> prog_freer(
+      &prog,
+      [](nvrtcProgram* p) { CHECK_NVRTC_ERROR(nvrtcDestroyProgram(p)); });
+  for (const auto& name : kernel_names) {
+    CHECK_NVRTC_ERROR(nvrtcAddNameExpression(prog, name.c_str()));
   }
 
+  // Compile program.
+  std::vector<const char*> args;
+  bool use_sass = compiler_supports_device_sass(device);
+  std::string compute = fmt::format(
+      "--gpu-architecture={}_{}{}",
+      use_sass ? "sm" : "compute",
+      device.compute_capability_major(),
+      device.compute_capability_minor());
+  args.push_back(compute.c_str());
+  std::string cccl_include = cccl_dir();
+  if (!cccl_include.empty()) {
+    cccl_include = fmt::format("--include-path={}", cccl_include);
+    args.push_back(cccl_include.c_str());
+  }
+  std::string cuda_include =
+      fmt::format("--include-path={}/include", cuda_home());
+  args.push_back(cuda_include.c_str());
+  nvrtcResult compile_result =
+      nvrtcCompileProgram(prog, args.size(), args.data());
+  if (compile_result != NVRTC_SUCCESS) {
+    size_t log_size;
+    CHECK_NVRTC_ERROR(nvrtcGetProgramLogSize(prog, &log_size));
+    std::vector<char> log(log_size + 1, 0);
+    CHECK_NVRTC_ERROR(nvrtcGetProgramLog(prog, log.data()));
+    throw std::runtime_error(
+        fmt::format("Failed to compile kernel: {}.", log.data()));
+  }
+
+  // Get mangled names of kernel names.
+  for (const auto& name : kernel_names) {
+    const char* mangled;
+    CHECK_NVRTC_ERROR(nvrtcGetLoweredName(prog, name.c_str(), &mangled));
+    ptx_kernels.emplace_back(name, mangled);
+  }
+
+  // Get ptx data.
+  size_t ptx_size;
+  if (use_sass) {
+    CHECK_NVRTC_ERROR(nvrtcGetCUBINSize(prog, &ptx_size));
+  } else {
+    CHECK_NVRTC_ERROR(nvrtcGetPTXSize(prog, &ptx_size));
+  }
+  ptx.resize(ptx_size);
+  if (use_sass) {
+    CHECK_NVRTC_ERROR(nvrtcGetCUBIN(prog, ptx.data()));
+  } else {
+    CHECK_NVRTC_ERROR(nvrtcGetPTX(prog, ptx.data()));
+  }
+}
+
+void load_module(
+    const std::string& module_name,
+    const std::string& ptx,
+    const std::vector<std::pair<std::string, std::string>>& ptx_kernels,
+    CUmodule& module_,
+    std::unordered_map<std::string, std::pair<CUfunction, bool>>& kernels) {
   // Load module.
   char jit_log[4089] = {};
   CUjit_option options[] = {
@@ -313,31 +312,85 @@ JitModule::JitModule(
   for (const auto& [name, mangled] : ptx_kernels) {
     CUfunction kernel;
     CHECK_CUDA_ERROR(cuModuleGetFunction(&kernel, module_, mangled.c_str()));
-    kernels_[name] = kernel;
+    kernels[name] = std::make_pair(kernel, false);
   }
 }
 
+} // namespace
+
+JitModule::JitModule(
+    Device& device,
+    const std::string& module_name,
+    const KernelBuilder& builder,
+    bool use_disk_cache) {
+  // Will hold the actual device executable source code and kernel names
+  std::string ptx;
+  std::vector<std::pair<std::string, std::string>> ptx_kernels;
+
+  // Try to load them from the file cache
+  if (!read_cached_ptx(ptx_cache_dir(), module_name, ptx, ptx_kernels)) {
+    auto [precompiled, source_code, kernel_names] = builder();
+
+    // Get the PTX or cubin
+    if (precompiled) {
+      ptx = std::move(source_code);
+      for (auto& name : kernel_names) {
+        ptx_kernels.emplace_back(name, name);
+      }
+    } else {
+      compile(device, module_name, source_code, kernel_names, ptx, ptx_kernels);
+    }
+
+    // If requested save them in the file cache for the next launch
+    if (use_disk_cache) {
+      write_cached_ptx(
+          ptx_cache_dir(), module_name, ptx, ptx_kernels, source_code);
+    }
+  }
+
+  // Load the module
+  load_module(module_name, ptx, ptx_kernels, module_, kernels_);
+}
+
 JitModule::~JitModule() {
   CHECK_CUDA_ERROR(cuModuleUnload(module_));
 }
 
-CUfunction JitModule::get_kernel(const std::string& kernel_name) {
+CUfunction JitModule::get_kernel(
+    const std::string& kernel_name,
+    std::function<void(CUfunction)> configure_kernel) {
   auto it = kernels_.find(kernel_name);
   if (it == kernels_.end()) {
     throw std::runtime_error(
         fmt::format("There is no kernel named {}.", kernel_name));
   }
-  return it->second;
+
+  // If it is the first time we run this kernel then configure it. Do it only
+  // once!
+  if (!it->second.second) {
+    if (configure_kernel) {
+      configure_kernel(it->second.first);
+    }
+    it->second.second = true;
+  }
+
+  return it->second.first;
+}
+
+std::unordered_map<std::string, JitModule>& get_jit_module_cache() {
+  static std::unordered_map<std::string, JitModule> map;
+  return map;
 }
 
 JitModule& get_jit_module(
     const mlx::core::Device& device,
     const std::string& name,
-    const KernelBuilder& builder) {
-  static std::unordered_map<std::string, JitModule> map;
+    const KernelBuilder& builder,
+    bool cache) {
+  auto& map = get_jit_module_cache();
   auto it = map.find(name);
   if (it == map.end()) {
-    it = map.try_emplace(name, cu::device(device), name, builder).first;
+    it = map.try_emplace(name, cu::device(device), name, builder, cache).first;
   }
   return it->second;
 }

mlx/backend/cuda/jit_module.h

@@ -19,7 +19,8 @@ namespace mlx::core::cu {
 
 class Device;
 
-using KernelBuilderResult = std::pair<
+using KernelBuilderResult = std::tuple<
+    /* precompiled */ bool,
     /* source code */ std::string,
     /* kernel names */ std::vector<std::string>>;
 using KernelBuilder = std::function<KernelBuilderResult()>;
@@ -40,19 +41,14 @@ struct KernelArgs {
   }
 
   template <typename T>
-  void append(std::vector<T> vec) {
-    if (vec.empty()) {
-      // The nullptr can not be used as arg, pass something not null.
-      append(std::monostate{});
-    } else {
-      append_ptr(vec.data());
-      storage_.emplace_back(std::move(vec));
-    }
+  void append(SmallVector<T> vec) {
+    storage_.emplace_back(std::move(vec));
+    append_ptr(std::get<SmallVector<T>>(storage_.back()).data());
   }
 
   // Make sure the arg is copied to an array with size of NDIM.
   template <size_t NDIM = MAX_NDIM, typename T>
-  void append_ndim(std::vector<T> vec) {
+  void append_ndim(SmallVector<T> vec) {
     if (vec.size() > NDIM) {
       throw std::runtime_error(
           fmt::format("ndim can not be larger than {}.", NDIM));
@@ -68,17 +64,19 @@ struct KernelArgs {
  private:
   std::vector<void*> args_;
 
-  // The cuLaunchKernel API requires passing pointers to arguments so store
-  // temporary values untill kernel is launched.
+  // The cuGraphAddKernelNode API requires passing pointers to arguments so
+  // store temporary values until the node is created.
   using Arg = std::variant<
       std::monostate,
       CUdeviceptr,
+      bool,
       int32_t,
       uint32_t,
       int64_t,
-      std::vector<const void*>,
-      std::vector<int32_t>,
-      std::vector<int64_t>>;
+      float,
+      SmallVector<const void*>,
+      SmallVector<int32_t>,
+      SmallVector<int64_t>>;
   std::deque<Arg> storage_;
 };
 
@@ -87,21 +85,27 @@ class JitModule {
   JitModule(
       Device& device,
       const std::string& module_name,
-      const KernelBuilder& builder);
+      const KernelBuilder& builder,
+      bool cache);
   ~JitModule();
 
   JitModule(const JitModule&) = delete;
   JitModule& operator=(const JitModule&) = delete;
-  CUfunction get_kernel(const std::string& kernel_name);
+  CUfunction get_kernel(
+      const std::string& kernel_name,
+      std::function<void(CUfunction)> configure_kernel = nullptr);
 
  private:
   CUmodule module_{nullptr};
-  std::unordered_map<std::string, CUfunction> kernels_;
+  std::unordered_map<std::string, std::pair<CUfunction, bool>> kernels_;
 };
 
+std::unordered_map<std::string, JitModule>& get_jit_module_cache();
+
 JitModule& get_jit_module(
     const mlx::core::Device& device,
     const std::string& name,
-    const KernelBuilder& builder);
+    const KernelBuilder& builder,
+    bool use_disk_cache = true);
 
 } // namespace mlx::core::cu

mlx/backend/cuda/kernel_utils.cu

@@ -30,4 +30,25 @@ std::pair<dim3, dim3> get_grid_and_block(int dim0, int dim1, int dim2) {
   return std::make_pair(dim3(gx, gy, gz), dim3(bx, by, bz));
 }
 
+std::tuple<dim3, uint> get_launch_args(
+    size_t size,
+    const Shape& shape,
+    const Strides& strides,
+    bool large,
+    int work_per_thread) {
+  size_t nthreads = cuda::ceil_div(size, work_per_thread);
+  uint block_dim = 1024;
+  if (block_dim > nthreads) {
+    block_dim = nthreads;
+  }
+  dim3 num_blocks;
+  if (large) {
+    num_blocks = get_2d_grid_dims(shape, strides, work_per_thread);
+    num_blocks.x = cuda::ceil_div(num_blocks.x, block_dim);
+  } else {
+    num_blocks.x = cuda::ceil_div(nthreads, block_dim);
+  }
+  return std::make_tuple(num_blocks, block_dim);
+}
+
 } // namespace mlx::core

mlx/backend/cuda/kernel_utils.cuh

@@ -101,7 +101,7 @@ inline constexpr bool is_inexact_v = is_floating_v<T> || is_complex_v<T>;
 
 // Utility to copy data from vector to array in host.
 template <int NDIM = MAX_NDIM, typename T = int32_t>
-inline cuda::std::array<T, NDIM> const_param(const std::vector<T>& vec) {
+inline cuda::std::array<T, NDIM> const_param(const SmallVector<T>& vec) {
   if (vec.size() > NDIM) {
     throw std::runtime_error(
         fmt::format("ndim can not be larger than {}.", NDIM));
@@ -120,53 +120,19 @@ dim3 get_2d_grid_dims(
     size_t divisor);
 std::pair<dim3, dim3> get_grid_and_block(int dim0, int dim1, int dim2);
 
-// Return a block size that achieves maximum potential occupancy for kernel.
-template <typename T>
-inline uint max_occupancy_block_dim(T kernel) {
-  int _, block_dim;
-  if constexpr (std::is_same_v<T, CUfunction>) {
-    CHECK_CUDA_ERROR(
-        cuOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel, 0, 0, 0));
-  } else {
-    CHECK_CUDA_ERROR(
-        cudaOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel));
-  }
-  return block_dim;
-}
-
 // Get the num_blocks and block_dims that maximize occupancy for |kernel|,
 // assuming each thread handles |work_per_thread| elements of |arr|.
-template <typename T>
-inline std::tuple<dim3, uint> get_launch_args(
-    T kernel,
+std::tuple<dim3, uint> get_launch_args(
     size_t size,
     const Shape& shape,
     const Strides& strides,
     bool large,
-    int work_per_thread = 1) {
-  size_t nthreads = cuda::ceil_div(size, work_per_thread);
-  uint block_dim = max_occupancy_block_dim(kernel);
-  if (block_dim > nthreads) {
-    block_dim = nthreads;
-  }
-  dim3 num_blocks;
-  if (large) {
-    num_blocks = get_2d_grid_dims(shape, strides, work_per_thread);
-    num_blocks.x = cuda::ceil_div(num_blocks.x, block_dim);
-  } else {
-    num_blocks.x = cuda::ceil_div(nthreads, block_dim);
-  }
-  return std::make_tuple(num_blocks, block_dim);
-}
+    int work_per_thread = 1);
 
-template <typename T>
-inline std::tuple<dim3, uint> get_launch_args(
-    T kernel,
-    const array& arr,
-    bool large,
-    int work_per_thread = 1) {
+inline std::tuple<dim3, uint>
+get_launch_args(const array& arr, bool large, int work_per_thread = 1) {
   return get_launch_args(
-      kernel, arr.size(), arr.shape(), arr.strides(), large, work_per_thread);
+      arr.size(), arr.shape(), arr.strides(), large, work_per_thread);
 }
 
 } // namespace mlx::core

mlx/backend/cuda/layer_norm.cu

@@ -1,7 +1,6 @@
 // Copyright © 2025 Apple Inc.
 
 #include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
 #include "mlx/backend/cuda/kernel_utils.cuh"
 #include "mlx/backend/cuda/reduce/reduce.cuh"
 #include "mlx/backend/gpu/copy.h"
@@ -11,8 +10,6 @@
 #include <cooperative_groups.h>
 #include <cooperative_groups/reduce.h>
 #include <nvtx3/nvtx3.hpp>
-#include <cub/block/block_load.cuh>
-#include <cub/block/block_reduce.cuh>
 
 namespace mlx::core {
 
@@ -75,9 +72,11 @@ __global__ void layer_norm(
   float sum = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS] = {};
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    sum += static_cast<float>(cub::ThreadReduce(xn, cuda::std::plus<>{}));
+    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+#pragma unroll
+    for (int i = 0; i < N_READS; ++i) {
+      sum += static_cast<float>(xn[i]);
+    }
   }
   sum = BlockReduceT{block, temp}.Sum(sum);
 
@@ -88,11 +87,18 @@ __global__ void layer_norm(
   float normalizer = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size, mean);
-    for (int i = 0; i < N_READS; ++i) {
-      float t = static_cast<float>(xn[i]) - mean;
-      normalizer += t * t;
+    if ((index + 1) * N_READS <= axis_size) {
+      auto xn = load_vector<N_READS>(x, index);
+#pragma unroll
+      for (int i = 0; i < N_READS; ++i) {
+        float t = static_cast<float>(xn[i]) - mean;
+        normalizer += t * t;
+      }
+    } else {
+      for (int i = index * N_READS; i < axis_size; ++i) {
+        float t = static_cast<float>(x[i]) - mean;
+        normalizer += t * t;
+      }
     }
   }
   normalizer = BlockReduceT{block, temp}.Sum(normalizer);
@@ -101,17 +107,15 @@ __global__ void layer_norm(
   // Outputs.
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    T wn[N_READS];
-    T bn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(b, b_stride), bn, axis_size);
+    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+    auto bn = load_vector<N_READS>(b, index, axis_size, b_stride, T(0));
+#pragma unroll
     for (int i = 0; i < N_READS; ++i) {
       float norm = (static_cast<float>(xn[i]) - mean) * normalizer;
       xn[i] = wn[i] * static_cast<T>(norm) + bn[i];
     }
-    cub::StoreDirectBlocked(index, out, xn, axis_size);
+    store_vector<N_READS>(out, index, xn, axis_size);
   }
 }
 
@@ -144,9 +148,11 @@ __global__ void layer_norm_vjp(
   float sum = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS] = {};
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    sum += static_cast<float>(cub::ThreadReduce(xn, cuda::std::plus<>{}));
+    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+#pragma unroll
+    for (int i = 0; i < N_READS; ++i) {
+      sum += static_cast<float>(xn[i]);
+    }
   }
   sum = BlockReduceF{block, temp.f}.Sum(sum);
 
@@ -156,19 +162,28 @@ __global__ void layer_norm_vjp(
   // Normalizer.
   float3 factors = {};
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
-    T xn[N_READS];
-    T wn[N_READS] = {};
-    T gn[N_READS] = {};
     auto index = r * BLOCK_DIM + block.thread_rank();
-    cub::LoadDirectBlocked(index, x, xn, axis_size, mean);
-    cub::LoadDirectBlocked(index, g, gn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
-    for (int i = 0; i < N_READS; i++) {
-      float t = static_cast<float>(xn[i]) - mean;
-      float wi = wn[i];
-      float gi = gn[i];
-      float wg = wi * gi;
-      factors = plus_f3(factors, {wg, wg * t, t * t});
+    auto gn = load_vector<N_READS>(g, index, axis_size, T(0));
+    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+
+    if ((index + 1) * N_READS <= axis_size) {
+      auto xn = load_vector<N_READS>(x, index);
+#pragma unroll
+      for (int i = 0; i < N_READS; ++i) {
+        float t = static_cast<float>(xn[i]) - mean;
+        float wi = wn[i];
+        float gi = gn[i];
+        float wg = wi * gi;
+        factors = plus_f3(factors, {wg, wg * t, t * t});
+      }
+    } else {
+      for (int i = index * N_READS; i < axis_size; ++i) {
+        float t = static_cast<float>(x[i]) - mean;
+        float wi = wn[i];
+        float gi = gn[i];
+        float wg = wi * gi;
+        factors = plus_f3(factors, {wg, wg * t, t * t});
+      }
     }
   }
   factors = BlockReduceF3{block, temp.f3}.Reduce(factors, plus_f3, {});
@@ -180,12 +195,10 @@ __global__ void layer_norm_vjp(
   // Outputs.
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
     auto index = r * BLOCK_DIM + block.thread_rank();
-    T xn[N_READS];
-    T wn[N_READS];
-    T gn[N_READS];
-    cub::LoadDirectBlocked(index, x, xn, axis_size);
-    cub::LoadDirectBlocked(index, g, gn, axis_size);
-    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
+    auto xn = load_vector<N_READS>(x, index, axis_size, T(0));
+    auto gn = load_vector<N_READS>(g, index, axis_size, T(0));
+    auto wn = load_vector<N_READS>(w, index, axis_size, w_stride, T(0));
+
     for (int i = 0; i < N_READS; i++) {
       float xi = (static_cast<float>(xn[i]) - mean) * normalizer;
       float wi = wn[i];
@@ -195,9 +208,9 @@ __global__ void layer_norm_vjp(
         wn[i] = gi * xi;
       }
     }
-    cub::StoreDirectBlocked(index, gx, xn, axis_size);
+    store_vector<N_READS>(gx, index, xn, axis_size);
     if constexpr (HAS_W) {
-      cub::StoreDirectBlocked(index, gw, wn, axis_size);
+      store_vector<N_READS>(gw, index, wn, axis_size);
     }
   }
 }
@@ -258,14 +271,15 @@ void LayerNorm::eval_gpu(
   encoder.set_input_array(b);
   encoder.set_output_array(out);
   dispatch_float_types(out.dtype(), "layernorm", [&](auto type_tag) {
-    constexpr uint32_t N_READS = 4;
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    constexpr int N_READS = 16 / sizeof(DataType);
     dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
       auto kernel = cu::layer_norm<DataType, block_dim(), N_READS>;
       encoder.add_kernel_node(
           kernel,
           n_rows,
           block_dim(),
+          0,
           x.data<DataType>(),
           w.data<DataType>(),
           b.data<DataType>(),
@@ -365,10 +379,10 @@ void LayerNormVJP::eval_gpu(
   encoder.set_output_array(gw_temp);
   dispatch_float_types(gx.dtype(), "layernorm_vjp", [&](auto type_tag) {
     dispatch_bool(has_w, [&](auto has_w_constant) {
-      constexpr int N_READS = 4;
+      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+      constexpr int N_READS = 16 / sizeof(DataType);
       dispatch_block_dim(
           cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-            using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
             auto kernel = cu::layer_norm_vjp<
                 DataType,
                 has_w_constant.value,
@@ -378,6 +392,7 @@ void LayerNormVJP::eval_gpu(
                 kernel,
                 n_rows,
                 block_dim(),
+                0,
                 x.data<DataType>(),
                 w.data<DataType>(),
                 g.data<DataType>(),

mlx/backend/cuda/logsumexp.cu

@@ -43,20 +43,19 @@ __global__ void logsumexp(const T* in, T* out, int axis_size) {
   AccT maxval = Limits<AccT>::finite_min();
   AccT normalizer = 0;
   for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); r++) {
-    AccT vals[N_READS];
-    cub::LoadDirectBlocked(
-        r * BLOCK_DIM + block.thread_rank(),
-        make_cast_iterator<AccT>(in),
-        vals,
-        axis_size,
-        Limits<AccT>::min());
+    auto index = r * BLOCK_DIM + block.thread_rank();
+    auto vals = load_vector<N_READS>(in, index, axis_size, Limits<T>::min());
     prevmax = maxval;
-    maxval = max_op(maxval, cub::ThreadReduce(vals, max_op));
+#pragma unroll
+    for (int i = 0; i < N_READS; ++i) {
+      maxval = max_op(maxval, static_cast<AccT>(vals[i]));
+    }
     // Online normalizer calculation for softmax:
     // https://github.com/NVIDIA/online-softmax
     normalizer = normalizer * softmax_exp(prevmax - maxval);
     for (int i = 0; i < N_READS; i++) {
-      normalizer = normalizer + softmax_exp(vals[i] - maxval);
+      normalizer =
+          normalizer + softmax_exp(static_cast<AccT>(vals[i]) - maxval);
     }
   }
 
@@ -143,14 +142,15 @@ void LogSumExp::eval_gpu(const std::vector<array>& inputs, array& out) {
   encoder.set_input_array(in);
   encoder.set_output_array(out);
   dispatch_float_types(out.dtype(), "logsumexp", [&](auto type_tag) {
-    constexpr int N_READS = 4;
+    using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+    constexpr int N_READS = 16 / sizeof(DataType);
     dispatch_block_dim(cuda::ceil_div(axis_size, N_READS), [&](auto block_dim) {
-      using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
       auto kernel = cu::logsumexp<DataType, float, block_dim(), N_READS>;
       encoder.add_kernel_node(
           kernel,
           n_rows,
           block_dim(),
+          0,
           in.data<DataType>(),
           out.data<DataType>(),
           axis_size);

mlx/backend/cuda/lru_cache.h

@@ -0,0 +1,159 @@
+// Copyright © 2025 Apple Inc.
+
+#pragma once
+
+#include <cstring>
+#include <list>
+#include <unordered_map>
+#include <utility>
+
+namespace mlx::core {
+
+template <
+    typename K,
+    typename V,
+    template <typename...> typename M = std::unordered_map>
+class LRUCache {
+ public:
+  using value_type = std::pair<K, V>;
+  using list_type = std::list<value_type>;
+  using iterator = typename list_type::iterator;
+  using const_iterator = typename list_type::const_iterator;
+  using map_type = M<K, iterator>;
+
+  explicit LRUCache(size_t capacity) : capacity_(capacity) {
+    if (capacity == 0) {
+      throw std::runtime_error("LRUCache requires capacity > 0.");
+    }
+  }
+
+  size_t size() const {
+    return map_.size();
+  }
+  size_t capacity() const {
+    return capacity_;
+  }
+  bool empty() const {
+    return vlist_.empty();
+  }
+
+  void resize(size_t new_capacity) {
+    capacity_ = new_capacity;
+    trim();
+  }
+
+  iterator begin() {
+    return vlist_.begin();
+  }
+  const_iterator begin() const {
+    return vlist_.begin();
+  }
+  iterator end() {
+    return vlist_.end();
+  }
+  const_iterator end() const {
+    return vlist_.end();
+  }
+
+  void clear() {
+    map_.clear();
+    vlist_.clear();
+  }
+
+  iterator find(const K& key) {
+    auto it = map_.find(key);
+    if (it == map_.end())
+      return end();
+    vlist_.splice(vlist_.begin(), vlist_, it->second);
+    return it->second;
+  }
+
+  template <typename U>
+  std::pair<iterator, bool> emplace(const K& key, U&& value) {
+    auto it = map_.find(key);
+    if (it != map_.end()) {
+      vlist_.splice(vlist_.begin(), vlist_, it->second);
+      return {it->second, false};
+    }
+
+    vlist_.emplace_front(key, std::forward<U>(value));
+    map_[key] = vlist_.begin();
+
+    trim();
+
+    return {vlist_.begin(), true};
+  }
+
+  iterator erase(iterator pos) {
+    map_.erase(pos->first);
+    return vlist_.erase(pos);
+  }
+
+  V& operator[](const K& key) {
+    auto it = find(key);
+    if (it == end()) {
+      it = emplace(key, V{}).first;
+    }
+    return it->second;
+  }
+
+ private:
+  void trim() {
+    while (map_.size() > capacity_) {
+      auto last = std::prev(vlist_.end());
+      map_.erase(last->first);
+      vlist_.pop_back();
+    }
+  }
+
+  list_type vlist_;
+  map_type map_;
+  size_t capacity_;
+};
+
+// Turn a POD struct into a container key by doing bytes compare.
+template <typename T>
+struct BytesKey {
+  T pod;
+  static_assert(std::is_standard_layout_v<T>, "T is not POD");
+
+  BytesKey(T pod) : pod(std::move(pod)) {}
+
+  BytesKey(const BytesKey& other) {
+    memcpy(&pod, &other.pod, sizeof(T));
+  }
+
+  BytesKey(BytesKey&& other) {
+    memcpy(&pod, &other.pod, sizeof(T));
+  }
+
+  bool operator==(const BytesKey& other) const {
+    auto* ptr1 = reinterpret_cast<const uint8_t*>(&pod);
+    auto* ptr2 = reinterpret_cast<const uint8_t*>(&other.pod);
+    return memcmp(ptr1, ptr2, sizeof(T)) == 0;
+  }
+};
+
+// Compute hash according to the bytes value of T.
+template <typename T>
+struct BytesHash {
+  static_assert(std::is_standard_layout_v<T>, "T is not POD");
+
+  size_t operator()(const T& pod) const {
+    auto* ptr = reinterpret_cast<const uint8_t*>(&pod);
+    uint32_t value = 0x811C9DC5;
+    for (int i = 0; i < sizeof(T); ++i) {
+      value ^= ptr[i];
+      value *= 0x01000193;
+    }
+    return value;
+  }
+};
+
+template <typename K, typename V>
+using BytesKeyHashMap = std::unordered_map<K, V, BytesHash<K>>;
+
+template <typename K, typename V>
+using LRUBytesKeyCache = LRUCache<BytesKey<K>, V, BytesKeyHashMap>;
+
+} // namespace mlx::core