Move pip install to python deps

Verbose install
Remove unnecessary uv
2025-12-16 01:49:05 +08:00 · 2025-07-30 17:39:09 +09:00 · 2025-07-30 16:57:43 +09:00 · 2025-07-30 15:30:54 +09:00 · 2025-07-30 15:27:00 +09:00 · 2025-07-30 15:20:40 +09:00
276 changed files with 3619 additions and 14016 deletions
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -18,14 +18,13 @@ jobs:
        type: boolean
        default: false
    macos:
-      xcode: "26.0.0"
-    resource_class: m4pro.medium
+      xcode: "16.2.0"
+    resource_class: m2pro.medium
    steps:
      - checkout
      - run:
          name: Install
          command: |
-            xcodebuild -downloadComponent MetalToolchain
            brew install python@3.9
            brew install doxygen
            python3.9 -m venv env
@@ -90,7 +89,6 @@ jobs:
          command: |
            uv venv
            uv pip install cmake
-            DEBUG=1 CMAKE_ARGS="-DCMAKE_COMPILE_WARNING_AS_ERROR=ON" \
            uv pip install -e ".[dev]" -v
      - run:
          name: Generate package stubs
@@ -120,7 +118,7 @@ jobs:
    parameters:
      xcode_version:
        type: string
-        default: "26.0.0"
+        default: "16.2.0"
      macosx_deployment_target:
        type: string
        default: ""
@@ -128,13 +126,12 @@ jobs:
      xcode: << parameters.xcode_version >>
    environment:
      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
-    resource_class: m4pro.medium
+    resource_class: m2pro.medium
    steps:
      - checkout
      - run:
          name: Install dependencies
          command: |
-            xcodebuild -downloadComponent MetalToolchain
            HOMEBREW_NO_AUTO_UPDATE=1 HOMEBREW_NO_INSTALL_CLEANUP=1 \
              brew install openmpi uv
      - run:
@@ -167,11 +164,9 @@ jobs:
      - run:
          name: Build example extension
          command: |
-            source .venv/bin/activate
            cd examples/extensions
            uv pip install -r requirements.txt
-            uv run --no-project setup.py build_ext --inplace
-            uv run --no-project python test.py
+            uv run --no-project setup.py build_ext -j8
      - store_test_results:
          path: test-results
      - run:
@@ -199,7 +194,7 @@ jobs:
          name: Run Python tests with JIT
          command: |
            CMAKE_ARGS="-DMLX_METAL_JIT=ON" \
-              uv pip install -e . -v
+              uv pip install -e .
            LOW_MEMORY=1 DEVICE=gpu METAL_DEVICE_WRAPPER_TYPE=1 \
              METAL_DEBUG_ERROR_MODE=0 \
              uv run --no-project python -m xmlrunner discover \
@@ -225,20 +220,15 @@ jobs:
            sudo apt-get update
            sudo apt-get install libcudnn9-dev-cuda-12
            sudo apt-get install libblas-dev liblapack-dev liblapacke-dev
-            sudo apt-get install libnccl2 libnccl-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: Set CCache size
-          command: ccache --max-size 1G
      - run:
          name: Install Python package
          command: |
            uv venv
-            uv pip install cmake
-            DEBUG=1 CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_COMPILE_WARNING_AS_ERROR=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
+            CMAKE_ARGS="-DMLX_BUILD_CUDA=ON -DCMAKE_CUDA_COMPILER=`which nvcc`" \
              uv pip install -e ".[dev]" -v
      - run:
          name: Run Python tests
@@ -246,23 +236,12 @@ jobs:
            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: Build CPP only
-          command: |
-            source .venv/bin/activate
-            cmake . -B build \
-              -DMLX_BUILD_CUDA=ON \
-              -DCMAKE_CUDA_COMPILER=`which nvcc` \
-              -DCMAKE_BUILD_TYPE=DEBUG
-            cmake --build build -j `nproc`
-      - run:
-          name: Run CPP tests
-          command: ./build/tests/tests -sfe="*fft_tests.cpp,*linalg_tests.cpp"
      - 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 }}
@@ -276,7 +255,7 @@ jobs:
        default: "3.9"
      xcode_version:
        type: string
-        default: "26.0.0"
+        default: "16.2.0"
      build_env:
        type: string
        default: ""
@@ -285,7 +264,7 @@ jobs:
        default: ""
    macos:
      xcode: << parameters.xcode_version >>
-    resource_class: m4pro.medium
+    resource_class: m2pro.medium
    environment:
      MACOSX_DEPLOYMENT_TARGET: << parameters.macosx_deployment_target >>
    steps:
@@ -293,15 +272,11 @@ jobs:
      - run:
          name: Install dependencies
          command: |
-            xcodebuild -downloadComponent MetalToolchain
-            mkdir -p ~/miniconda3
-            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o ~/miniconda3/miniconda.sh
-            bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
-            rm ~/miniconda3/miniconda.sh
-            source ~/miniconda3/bin/activate
-            conda init --all
-            conda create -n env python=<< parameters.python_version >> -y
-            conda activate env
+            brew install python@<< parameters.python_version >>
+            brew install openmpi
+            python<< parameters.python_version >> -m venv env
+            source env/bin/activate
+            pip install --upgrade pip
            pip install --upgrade cmake
            pip install nanobind==2.4.0
            pip install --upgrade setuptools
@@ -311,19 +286,19 @@ jobs:
      - run:
          name: Install Python package
          command: |
-            conda activate env
+            source env/bin/activate
            env -u MACOSX_DEPLOYMENT_TARGET DEV_RELEASE=1 \
              pip install . -v
      - run:
          name: Generate package stubs
          command: |
-            conda activate env
+            source env/bin/activate
            pip install typing_extensions
            python setup.py generate_stubs
      - run:
          name: Build Python package
          command: |
-            conda activate env
+            source env/bin/activate
            python setup.py clean --all
            << parameters.build_env >> MLX_BUILD_STAGE=1 python -m build -w
      - when:
@@ -333,7 +308,7 @@ jobs:
            - run:
                name: Build common package
                command: |
-                  conda activate env
+                  source env/bin/activate
                  python setup.py clean --all
                  << parameters.build_env >> MLX_BUILD_STAGE=2 python -m build -w
      - when:
@@ -342,7 +317,7 @@ jobs:
            - run:
                name: Upload package
                command: |
-                  conda activate env
+                  source env/bin/activate
                  twine upload dist/*
      - store_artifacts:
          path: dist/
@@ -415,7 +390,7 @@ jobs:
        default: ""
    machine:
      image: ubuntu-2204:current
-      resource_class: xlarge
+      resource_class: large
    steps:
      - checkout
      - run:
@@ -462,7 +437,7 @@ workflows:
      - mac_build_and_test:
          matrix:
            parameters:
-              macosx_deployment_target: ["13.5", "15.0"]
+              macosx_deployment_target: ["13.5", "14.0"]
      - linux_build_and_test
      - cuda_build_and_test:
          matrix:
@@ -487,7 +462,68 @@ workflows:
              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
              macosx_deployment_target: ["13.5", "14.0", "15.0"]
              build_env: ["PYPI_RELEASE=1"]
-              xcode_version: ["26.0.0"]
+              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: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+                build_env: "PYPI_RELEASE=1"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+                build_env: "PYPI_RELEASE=1"
      - build_documentation:
          filters:
            tags:
@@ -529,7 +565,7 @@ workflows:
          requires: [ hold ]
          matrix:
            parameters:
-              macosx_deployment_target: ["13.5", "15.0"]
+              macosx_deployment_target: ["13.5", "14.0"]
      - linux_build_and_test:
          requires: [ hold ]
      - cuda_build_and_test:
@@ -548,7 +584,53 @@ workflows:
            parameters:
              python_version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
              macosx_deployment_target: ["13.5", "14.0", "15.0"]
-              xcode_version: ["26.0.0"]
+              xcode_version: ["16.2.0", "15.0.0"]
+            exclude:
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "13.5"
+                xcode_version: "16.2.0"
+                python_version: "3.13"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "14.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.9"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.10"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.11"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.12"
+              - macosx_deployment_target: "15.0"
+                xcode_version: "15.0.0"
+                python_version: "3.13"
      - build_linux_release:
          matrix:
            parameters:
@@ -567,7 +649,68 @@ workflows:
              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: ["26.0.0"]
+              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:
--- a/ACKNOWLEDGMENTS.md
+++ b/ACKNOWLEDGMENTS.md
@@ -19,17 +19,12 @@ MLX was developed with contributions from the following individuals:
 - Gleb Pobudzey: Added the `where` primitive, and groups in 1D and 2D convolutions.
 - Paul Paczuski: Improved stability of BCE loss calculation
 - Max-Heinrich Laves: Added `conv_transpose1d`, `conv_transpose2d`, and `conv_transpose3d` ops.
- Gökdeniz Gülmez: Added the `Muon (MomentUm Orthogonalized by Newton-schulz)` optimizer, and the `ReLU²` activation function.
+- Gökdeniz Gülmez: Added the `Muon (MomentUm Orthogonalized by Newton-schulz)` optimizer.

 <a href="https://github.com/ml-explore/mlx/graphs/contributors">
  <img class="dark-light" src="https://contrib.rocks/image?repo=ml-explore/mlx&anon=0&columns=20&max=100&r=true" />
 </a>

-# Organizations
-
-MLX has received contributions from the following companies:
- NVIDIA Corporation & Affiliates
-
 # Third-Party Software

 MLX leverages several third-party software, listed here together with
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -43,7 +43,6 @@ 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(
@@ -87,21 +86,22 @@ cmake_policy(SET CMP0135 NEW)

 add_library(mlx)

+if(MLX_BUILD_METAL)
+  set(METAL_LIB "-framework Metal")
+  set(FOUNDATION_LIB "-framework Foundation")
+  set(QUARTZ_LIB "-framework QuartzCore")
+endif()
+
 if(MLX_BUILD_CUDA)
  enable_language(CUDA)
 endif()

-if(MLX_BUILD_METAL)
-  find_library(METAL_LIB Metal)
-  find_library(FOUNDATION_LIB Foundation)
-  find_library(QUARTZ_LIB QuartzCore)
-  if(METAL_LIB)
-    message(STATUS "Metal found ${METAL_LIB}")
-  else()
-    message(
-      FATAL_ERROR
-        "Metal not found. Set MLX_BUILD_METAL=OFF to build without GPU")
-  endif()
+if(MLX_BUILD_METAL AND NOT METAL_LIB)
+  message(STATUS "Metal not found. Unable to build GPU")
+  set(MLX_BUILD_METAL OFF)
+  set(MLX_METAL_DEBUG OFF)
+elseif(MLX_BUILD_METAL)
+  message(STATUS "Building METAL sources")

  if(MLX_METAL_DEBUG)
    add_compile_definitions(MLX_METAL_DEBUG)
@@ -110,8 +110,7 @@ if(MLX_BUILD_METAL)
  # Throw an error if xcrun not found
  execute_process(
    COMMAND zsh "-c" "/usr/bin/xcrun -sdk macosx --show-sdk-version"
-    OUTPUT_VARIABLE MACOS_SDK_VERSION
-    OUTPUT_STRIP_TRAILING_WHITESPACE COMMAND_ERROR_IS_FATAL ANY)
+    OUTPUT_VARIABLE MACOS_SDK_VERSION COMMAND_ERROR_IS_FATAL ANY)

  if(${MACOS_SDK_VERSION} LESS 14.0)
    message(
@@ -140,12 +139,6 @@ if(MLX_BUILD_METAL)
  target_link_libraries(mlx PUBLIC ${METAL_LIB} ${FOUNDATION_LIB} ${QUARTZ_LIB})
 endif()

-if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
-  # With newer clang/gcc versions following libs are implicitly linked, but when
-  # building on old distributions they need to be explicitly listed.
-  target_link_libraries(mlx PRIVATE dl pthread)
-endif()
-
 if(WIN32)
  if(MSVC)
    # GGUF does not build with MSVC.
@@ -249,16 +242,12 @@ target_include_directories(
 # Do not add mlx_EXPORTS define for shared library.
 set_target_properties(mlx PROPERTIES DEFINE_SYMBOL "")

-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)
--- a/README.md
+++ b/README.md
@@ -78,13 +78,13 @@ pip install mlx
 To install the CUDA backend on Linux, run:

 ```bash
-pip install mlx[cuda]
+pip install "mlx[cuda]"
 ```

 To install a CPU-only Linux package, run:

 ```bash
-pip install mlx[cpu]
+pip install "mlx[cpu]"
 ```

 Checkout the
--- a/cmake/FindNCCL.cmake
+++ b/cmake/FindNCCL.cmake
@@ -1,54 +0,0 @@
-# FindNCCL.cmake This module finds the NVIDIA NCCL library and its include
-# directories.
-
-set(NCCL_ROOT_DIR
-    $ENV{NCCL_ROOT_DIR}
-    CACHE PATH "Folder contains NVIDIA NCCL")
-
-find_path(
-  NCCL_INCLUDE_DIRS
-  NAMES nccl.h
-  HINTS ${NCCL_INCLUDE_DIR} ${NCCL_ROOT_DIR} ${NCCL_ROOT_DIR}/include
-        ${CUDA_TOOLKIT_ROOT_DIR}/include)
-
-if($ENV{USE_STATIC_NCCL})
-  message(
-    STATUS "USE_STATIC_NCCL detected. Linking against static NCCL library")
-  set(NCCL_LIBNAME "libnccl_static.a")
-else()
-  set(NCCL_LIBNAME "nccl")
-endif()
-
-find_library(
-  NCCL_LIBRARIES
-  NAMES ${NCCL_LIBNAME}
-  HINTS ${NCCL_LIB_DIR}
-        ${NCCL_ROOT_DIR}
-        ${NCCL_ROOT_DIR}/lib
-        ${NCCL_ROOT_DIR}/lib/x86_64-linux-gnu
-        ${NCCL_ROOT_DIR}/lib64
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib64)
-
-include(FindPackageHandleStandardArgs)
-find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIRS
-                                  NCCL_LIBRARIES)
-
-if(NCCL_FOUND)
-  set(NCCL_HEADER_FILE "${NCCL_INCLUDE_DIRS}/nccl.h")
-  message(
-    STATUS "Determining NCCL version from the header file: ${NCCL_HEADER_FILE}")
-  file(
-    STRINGS ${NCCL_HEADER_FILE} NCCL_MAJOR_VERSION_DEFINED
-    REGEX "^[ \t]*#define[ \t]+NCCL_MAJOR[ \t]+[0-9]+.*$"
-    LIMIT_COUNT 1)
-  if(NCCL_MAJOR_VERSION_DEFINED)
-    string(REGEX REPLACE "^[ \t]*#define[ \t]+NCCL_MAJOR[ \t]+" ""
-                         NCCL_MAJOR_VERSION ${NCCL_MAJOR_VERSION_DEFINED})
-    message(STATUS "NCCL_MAJOR_VERSION: ${NCCL_MAJOR_VERSION}")
-  endif()
-  message(
-    STATUS
-      "Found NCCL (include: ${NCCL_INCLUDE_DIRS}, library: ${NCCL_LIBRARIES})")
-  mark_as_advanced(NCCL_ROOT_DIR NCCL_INCLUDE_DIRS NCCL_LIBRARIES)
-endif()
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,5 +1,4 @@
 sphinx
 breathe
 sphinx-book-theme
-sphinx-copybutton
 mlx
--- a/docs/src/conf.py
+++ b/docs/src/conf.py
@@ -18,7 +18,6 @@ release = version
 # -- General configuration ---------------------------------------------------

 extensions = [
-    "sphinx_copybutton",
    "sphinx.ext.autodoc",
    "sphinx.ext.autosummary",
    "sphinx.ext.intersphinx",
--- a/docs/src/dev/custom_metal_kernels.rst
+++ b/docs/src/dev/custom_metal_kernels.rst
@@ -127,8 +127,7 @@ relying on a copy from ``ensure_row_contiguous``:
      name="myexp_strided",
      input_names=["inp"],
      output_names=["out"],
-      source=source,
-      ensure_row_contiguous=False,
+      source=source
  )

  def exp_elementwise(a: mx.array):
@@ -139,6 +138,7 @@ 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]

--- a/docs/src/dev/extensions.rst
+++ b/docs/src/dev/extensions.rst
@@ -394,14 +394,14 @@ below.
        out.set_data(allocator::malloc(out.nbytes()));

        // Resolve name of kernel
-        std::stream kname;
-        kname = "axpby_general_" + type_to_name(out);
+        std::ostringstream kname;
+        kname << "axpby_" << "general_" << type_to_name(out);

        // Load the metal library
-        auto lib = d.get_library("mlx_ext", current_binary_dir());
+        auto lib = d.get_library("mlx_ext");

        // Make a kernel from this metal library
-        auto kernel = d.get_kernel(kname, lib);
+        auto kernel = d.get_kernel(kname.str(), lib);

        // Prepare to encode kernel
        auto& compute_encoder = d.get_command_encoder(s.index);
--- a/docs/src/index.rst
+++ b/docs/src/index.rst
@@ -70,7 +70,6 @@ are the CPU and GPU.
   python/fft
   python/linalg
   python/metal
-   python/cuda
   python/memory_management
   python/nn
   python/optimizers
--- a/docs/src/install.rst
+++ b/docs/src/install.rst
@@ -30,7 +30,7 @@ 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:
@@ -49,7 +49,7 @@ 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:
@@ -271,7 +271,7 @@ and the CUDA toolkit. For example on Ubuntu, run the following:
   dpkg -i cuda-keyring_1.1-1_all.deb
   apt-get update -y
   apt-get -y install cuda-toolkit-12-9
-   apt-get install libblas-dev liblapack-dev liblapacke-dev libcudnn9-dev-cuda-12 -y
+   apt-get install libblas-dev liblapack-dev liblapacke-dev -y


 When building either the Python or C++ APIs make sure to pass the cmake flag
--- a/docs/src/python/cuda.rst
+++ b/docs/src/python/cuda.rst
@@ -1,9 +0,0 @@
-CUDA
-=====
-
-.. currentmodule:: mlx.core.cuda
-
-.. autosummary::
-  :toctree: _autosummary
-
-  is_available
--- a/docs/src/python/fast.rst
+++ b/docs/src/python/fast.rst
@@ -13,4 +13,3 @@ Fast
  rope
  scaled_dot_product_attention
  metal_kernel
-  cuda_kernel
--- a/docs/src/python/nn/functions.rst
+++ b/docs/src/python/nn/functions.rst
@@ -27,7 +27,6 @@ simple functions.
   mish
   prelu
   relu
-   relu2
   relu6
   selu
   sigmoid
--- a/docs/src/python/nn/layers.rst
+++ b/docs/src/python/nn/layers.rst
@@ -50,7 +50,6 @@ Layers
   QuantizedLinear
   RMSNorm
   ReLU
-   ReLU2
   ReLU6
   RNN
   RoPE
--- a/docs/src/python/optimizers.rst
+++ b/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, destination={})
-   mx.save_safetensors("optimizer.safetensors", state)
+   state = tree_flatten(optimizer.state)
+   mx.save_safetensors("optimizer.safetensors", dict(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(mx.load("optimizer.safetensors"))
+   state = tree_unflatten(list(mx.load("optimizer.safetensors").items()))
   optimizer.state = state

 Note, not every optimizer configuation parameter is saved in the state. For
--- a/docs/src/usage/compile.rst
+++ b/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)
+      return mx.exp(z), state

  fun(mx.array(1.0), mx.array(2.0))
  # Prints [array(3, dtype=float32)]
--- a/docs/src/usage/export.rst
+++ b/docs/src/usage/export.rst
@@ -151,7 +151,7 @@ parameters, pass them as inputs to the ``call`` wrapper:
     model.update(tree_unflatten(list(params.items())))
     return model(x)
 
-   params = tree_flatten(model.parameters(), destination={})
+   params = dict(tree_flatten(model.parameters()))
   mx.export_function("model.mlxfn", call, (mx.zeros(4),), params)


--- a/docs/src/usage/indexing.rst
+++ b/docs/src/usage/indexing.rst
@@ -107,20 +107,8 @@ same array:
  >>> a
  array([1, 2, 0], dtype=int32)

-Note that unlike NumPy, slicing an array creates a copy, not a view. So
-mutating it does not mutate the original array:

-.. code-block:: shell
-
-  >>> a = mx.array([1, 2, 3])
-  >>> b = a[:]
-  >>> b[2] = 0
-  >>> b
-  array([1, 2, 0], dtype=int32)
-  >>> a
-  array([1, 2, 3], dtype=int32)
-
-Also unlike NumPy, updates to the same location are nondeterministic:
+Note, unlike NumPy, updates to the same location are nondeterministic:

 .. code-block:: shell

--- a/examples/extensions/axpby/axpby.cpp
+++ b/examples/extensions/axpby/axpby.cpp
@@ -1,6 +1,5 @@
 // Copyright © 2023-2025 Apple Inc.

-#include <dlfcn.h>
 #include <iostream>
 #include <sstream>

@@ -17,19 +16,6 @@

 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
 ///////////////////////////////////////////////////////////////////////////////
@@ -181,15 +167,16 @@ void Axpby::eval_gpu(
  }

  // Resolve name of kernel (corresponds to axpby.metal)
-  std::string kname = "axpby_";
-  kname += (contiguous_kernel ? "contiguous_" : "general_");
-  kname += type_to_name(out);
+  std::ostringstream kname;
+  kname << "axpby_";
+  kname << (contiguous_kernel ? "contiguous_" : "general_");
+  kname << type_to_name(out);

  // Load the metal library
-  auto lib = d.get_library("mlx_ext", current_binary_dir());
+  auto lib = d.get_library("mlx_ext");

  // Make a kernel from this metal library
-  auto kernel = d.get_kernel(kname, lib);
+  auto kernel = d.get_kernel(kname.str(), lib);

  // Prepare to encode kernel
  auto& compute_encoder = d.get_command_encoder(s.index);
--- a/examples/extensions/requirements.txt
+++ b/examples/extensions/requirements.txt
@@ -1,4 +1,4 @@
 setuptools>=42
 cmake>=3.25
 mlx>=0.21.0
-nanobind==2.4.0
+nanobind==2.2.0
--- a/examples/extensions/test.py
+++ b/examples/extensions/test.py
@@ -3,10 +3,8 @@ from mlx_sample_extensions import axpby

 a = mx.ones((3, 4))
 b = mx.ones((3, 4))
-c_cpu = axpby(a, b, 4.0, 2.0, stream=mx.cpu)
-c_gpu = axpby(a, b, 4.0, 2.0, stream=mx.gpu)
+c = axpby(a, b, 4.0, 2.0, stream=mx.cpu)

-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()}")
+print(f"c shape: {c.shape}")
+print(f"c dtype: {c.dtype}")
+print(f"c correct: {mx.all(c == 6.0).item()}")
--- a/mlx/array.h
+++ b/mlx/array.h
@@ -10,7 +10,6 @@
 #include "mlx/allocator.h"
 #include "mlx/dtype.h"
 #include "mlx/event.h"
-#include "mlx/small_vector.h"

 namespace mlx::core {

@@ -19,8 +18,8 @@ class Primitive;

 using Deleter = std::function<void(allocator::Buffer)>;
 using ShapeElem = int32_t;
-using Shape = SmallVector<ShapeElem>;
-using Strides = SmallVector<int64_t>;
+using Shape = std::vector<ShapeElem>;
+using Strides = std::vector<int64_t>;

 class array {
  /* An array is really a node in a graph. It contains a shared ArrayDesc
--- a/mlx/backend/common/utils.h
+++ b/mlx/backend/common/utils.h
@@ -197,7 +197,7 @@ void shared_buffer_reshape(
    array& out);

 template <typename T>
-inline SmallVector<T> remove_index(SmallVector<T> vec, size_t index) {
+inline std::vector<T> remove_index(std::vector<T> vec, size_t index) {
  vec.erase(std::next(vec.begin(), index));
  return vec;
 }
--- a/mlx/backend/cpu/compiled.cpp
+++ b/mlx/backend/cpu/compiled.cpp
@@ -15,7 +15,6 @@
 #include "mlx/backend/cpu/jit_compiler.h"
 #include "mlx/device.h"
 #include "mlx/graph_utils.h"
-#include "mlx/version.h"

 namespace mlx::core {

@@ -95,11 +94,7 @@ void* compile(
    kernel_file_name = kernel_name;
  }

-  auto output_dir =
-      std::filesystem::temp_directory_path() / "mlx" / version() / "cpu";
-  if (!std::filesystem::exists(output_dir)) {
-    std::filesystem::create_directories(output_dir);
-  }
+  auto output_dir = std::filesystem::temp_directory_path();

  std::string shared_lib_name = "lib" + kernel_file_name + ".so";
  auto shared_lib_path = (output_dir / shared_lib_name).string();
@@ -162,12 +157,10 @@ inline void build_kernel(
 #endif

  // Start the kernel
-  os << "void " << kernel_name
-     << "(int* shape, int64_t** strides, void** args) {" << std::endl;
+  os << "void " << kernel_name << "(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)) {
@@ -182,8 +175,8 @@ inline void build_kernel(
       << "];" << std::endl;
    // Scalars and contiguous need no strides
    if (!is_scalar(x) && !contiguous) {
-      os << "  const int64_t* " << xname << "_strides = strides["
-         << strides_index++ << "];" << std::endl;
+      os << "  const size_t* " << xname << "_strides = (size_t*)args[" << cnt++
+         << "];" << std::endl;
    }
  }

@@ -193,8 +186,10 @@ inline void build_kernel(
    os << "  " << tstr << "* " << namer.get_name(x) << " = (" << tstr
       << "*)args[" << cnt++ << "];" << std::endl;
  }
-  // Add output size
-  if (contiguous) {
+  // Add output strides and shape to extract the indices.
+  if (!contiguous) {
+    os << "  const int* shape = (int*)args[" << cnt++ << "];" << std::endl;
+  } else {
    os << "  const size_t size = (size_t)args[" << cnt++ << "];" << std::endl;
  }

@@ -295,6 +290,7 @@ 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;
@@ -302,6 +298,9 @@ 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
@@ -336,20 +335,16 @@ void Compiled::eval_cpu(
    args.push_back(x.data<void>());
    encoder.set_output_array(x);
  }
-  if (contiguous) {
+  if (!contiguous) {
+    args.push_back((void*)shape.data());
+  } else {
    args.push_back((void*)outputs[0].data_size());
  }
-  auto fun = reinterpret_cast<void (*)(int*, int64_t**, void**)>(fn_ptr);
+  auto fun = (void (*)(void**))fn_ptr;
  encoder.dispatch([fun,
                    args = std::move(args),
                    strides = std::move(strides),
-                    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());
-  });
+                    shape = std::move(shape)]() mutable { fun(args.data()); });
 }

 } // namespace mlx::core
--- a/mlx/backend/cpu/jit_compiler.cpp
+++ b/mlx/backend/cpu/jit_compiler.cpp
@@ -2,7 +2,6 @@

 #include "mlx/backend/cpu/jit_compiler.h"

-#include <algorithm>
 #include <sstream>
 #include <vector>

--- a/mlx/backend/cpu/lapack.h
+++ b/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(gesdd)
+INSTANTIATE_LAPACK_REAL(gesvdx)
 INSTANTIATE_LAPACK_REAL(getrf)
 INSTANTIATE_LAPACK_REAL(getri)
 INSTANTIATE_LAPACK_REAL(trtri)
--- a/mlx/backend/cpu/quantized.cpp
+++ b/mlx/backend/cpu/quantized.cpp
@@ -1,5 +1,7 @@
 // Copyright © 2023 Apple Inc.

+#include <cassert>
+
 #include "mlx/backend/cpu/copy.h"
 #include "mlx/backend/cpu/encoder.h"
 #include "mlx/backend/cpu/simd/simd.h"
@@ -11,35 +13,6 @@ namespace mlx::core {

 namespace {

-const static float MXFP4_LUT[16] = {
-    +0.0f,
-    +0.5f,
-    +1.0f,
-    +1.5f,
-    +2.0f,
-    +3.0f,
-    +4.0f,
-    +6.0f,
-    -0.0f,
-    -0.5f,
-    -1.0f,
-    -1.5f,
-    -2.0f,
-    -3.0f,
-    -4.0f,
-    -6.0f};
-
-template <typename T>
-static inline T dequantize_scale(uint8_t s) {
-  using FOrI = union {
-    bfloat16_t f;
-    uint16_t i;
-  };
-  FOrI out;
-  out.i = (s == 0 ? 0x40 : (static_cast<uint16_t>(s) << 7));
-  return static_cast<T>(out.f);
-}
-
 inline constexpr short get_pack_factor(int bits, int wsize = 8) {
  return (bits == 3 || bits == 5) ? 8 : (bits == 6 ? 4 : wsize / bits);
 }
@@ -434,231 +407,6 @@ void _qmm_dispatch(
  }
 }

-template <typename T>
-void mxfp4_qmm(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const uint8_t* scales,
-    int M,
-    int N,
-    int K) {
-  constexpr int group_size = 32;
-  constexpr int pack_factor = get_pack_factor(4, 8);
-  constexpr int bytes_per_pack = get_bytes_per_pack(4);
-  constexpr int packs_in_group = group_size / pack_factor;
-
-  for (int m = 0; m < M; m++) {
-    const uint8_t* w_local = (const uint8_t*)w;
-    const uint8_t* scales_local = scales;
-
-    std::fill(result, result + N, 0);
-
-    for (int k = 0; k < K; k++) {
-      T* result_local = result;
-      T xi = *x++;
-
-      for (int n = 0; n < N; n += group_size) {
-        T scale = dequantize_scale<T>(*scales_local++);
-        for (int ng = 0; ng < packs_in_group; ng++) {
-          uint8_t wi = *w_local++;
-#pragma clang loop unroll(full)
-          for (int p = 0; p < pack_factor; p++) {
-            (*result_local++) +=
-                xi * scale * static_cast<T>(MXFP4_LUT[wi & 0xf]);
-            wi >>= 4;
-          }
-        }
-      }
-    }
-
-    result += N;
-  }
-}
-
-template <typename T>
-void mxfp4_qmm_t(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const uint8_t* scales,
-    int M,
-    int N,
-    int K) {
-  constexpr int group_size = 32;
-  constexpr int pack_factor = get_pack_factor(4, 8);
-  constexpr int bytes_per_pack = get_bytes_per_pack(4);
-  constexpr int packs_in_group = group_size / pack_factor;
-
-  for (int m = 0; m < M; m++) {
-    const uint8_t* w_local = (const uint8_t*)w;
-    const uint8_t* scales_local = scales;
-
-    for (int n = 0; n < N; n++) {
-      const T* x_local = x;
-      T sum = 0;
-      for (int k = 0; k < K; k += group_size) {
-        T scale = dequantize_scale<T>(*scales_local++);
-
-        T gsum = 0;
-        for (int kw = 0; kw < packs_in_group; kw++) {
-          uint8_t wi = *w_local++;
-#pragma clang loop unroll(full)
-          for (int p = 0; p < pack_factor; p++) {
-            gsum += (*x_local++) * static_cast<T>(MXFP4_LUT[wi & 0xf]);
-            wi >>= 4;
-          }
-        }
-        sum += scale * gsum;
-      }
-      *result = sum;
-      result++;
-    }
-
-    x += K;
-  }
-}
-
-template <int S>
-simd::Simd<float, S> mxfp4_extract_bits_simd(const uint32_t* w) {
-  if constexpr (S == 8) {
-    constexpr std::array<uint32_t, 8> shifts_ = {{0, 4, 8, 12, 16, 20, 24, 28}};
-    auto shifts(*(simd::Simd<uint32_t, S>*)&shifts_);
-    auto wi = simd::Simd<uint32_t, S>(*w);
-    wi = wi >> shifts;
-    wi = wi & 0xf;
-    simd::Simd<float, S> w_out;
-    for (int i = 0; i < S; ++i) {
-      w_out[i] = MXFP4_LUT[wi[i]];
-    }
-    return w_out;
-  } else {
-    // Appease compiler.. but should never get here
-    throw std::runtime_error("Unsupported combination for simd qmm.");
-  }
-}
-
-template <typename T>
-void mxfp4_qmm_t_simd(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const uint8_t* scales,
-    int M,
-    int N,
-    int K) {
-  constexpr int group_size = 32;
-  constexpr int pack_factor = 32 / 4;
-  constexpr int packs_in_group = group_size / pack_factor;
-  constexpr int S = simd::max_size<T>;
-  static_assert(
-      S % pack_factor == 0, "SIMD size must be divisible by pack factor");
-  constexpr int packs_per_simd = S / pack_factor;
-
-  for (int m = 0; m < M; m++) {
-    const uint32_t* w_local = w;
-    const uint8_t* scales_local = scales;
-
-    for (int n = 0; n < N; n++) {
-      simd::Simd<float, S> acc(0);
-      auto x_local = x;
-      for (int k = 0; k < K; k += group_size) {
-        T scale = dequantize_scale<T>(*scales_local++);
-
-        simd::Simd<float, S> g_acc(0);
-        for (int kw = 0; kw < packs_in_group; kw += packs_per_simd) {
-          // Extract bits
-          auto wf = mxfp4_extract_bits_simd<S>(w_local);
-          w_local += packs_per_simd;
-          simd::Simd<float, S> x_simd = simd::load<T, S>(x_local);
-          g_acc = g_acc + x_simd * wf;
-          x_local += S;
-        }
-        acc = acc + scale * g_acc;
-      }
-
-      *result = T(simd::sum(acc));
-      result++;
-    }
-    x += K;
-  }
-}
-
-template <typename T>
-void mxfp4_qmm_dispatch_transpose(
-    T* result,
-    const T* x,
-    const uint32_t* w,
-    const uint8_t* scales,
-    int M,
-    int N,
-    int K,
-    bool transposed_w) {
-  if (transposed_w) {
-    // the simd size must be a multiple of the number of elements per word
-    if constexpr (simd::max_size<T> % 8 == 0) {
-      mxfp4_qmm_t_simd<T>(result, x, w, scales, M, N, K);
-    } else {
-      mxfp4_qmm_t<T>(result, x, w, scales, M, N, K);
-    }
-  } else {
-    mxfp4_qmm<T>(result, x, w, scales, M, N, K);
-  }
-}
-
-template <typename T>
-void mxfp4_qmm_dispatch_typed(
-    array& out,
-    const array& x,
-    const array& w,
-    const array& scales,
-    bool transposed_w) {
-  int K = x.shape(-1);
-  int M = x.ndim() > 1 ? x.shape(-2) : 1;
-  int N = out.shape(-1);
-  int w_els = w.ndim() > 2 ? w.shape(-1) * w.shape(-2) : 0;
-  int g_els = w.ndim() > 2 ? scales.shape(-1) * scales.shape(-2) : 0;
-  int batch_size = x.size() / (K * M);
-
-  auto out_ptr = out.data<T>();
-  auto x_ptr = x.data<T>();
-  auto w_ptr = w.data<uint32_t>();
-  auto scales_ptr = scales.data<uint8_t>();
-  for (int i = 0; i < batch_size; i++) {
-    mxfp4_qmm_dispatch_transpose<T>(
-        out_ptr + i * M * N,
-        x_ptr + elem_to_loc(i * M * K, x.shape(), x.strides()),
-        w_ptr + elem_to_loc(i * w_els, w.shape(), w.strides()),
-        scales_ptr + elem_to_loc(i * g_els, scales.shape(), scales.strides()),
-        M,
-        N,
-        K,
-        transposed_w);
-  }
-}
-
-void mxfp4_qmm_dispatch(
-    array& out,
-    const array& x,
-    const array& w,
-    const array& scales,
-    bool transposed_w) {
-  switch (x.dtype()) {
-    case bfloat16:
-      mxfp4_qmm_dispatch_typed<bfloat16_t>(out, x, w, scales, transposed_w);
-      break;
-    case float16:
-      mxfp4_qmm_dispatch_typed<float16_t>(out, x, w, scales, transposed_w);
-      break;
-    case float32:
-      mxfp4_qmm_dispatch_typed<float>(out, x, w, scales, transposed_w);
-      break;
-    default:
-      throw std::invalid_argument(
-          "[quantized_matmul] only floating types are supported");
-  }
-}
-
 template <typename T>
 void _bs_qmm_dispatch_typed(
    array& out,
@@ -765,106 +513,41 @@ void _bs_qmm_dispatch(
  }
 }

-template <typename T>
-void mxfp4_bs_qmm_dispatch_typed(
-    array& out,
-    const array& x,
-    const array& w,
-    const array& scales,
-    const array& lhs_indices,
-    const array& rhs_indices,
-    bool transposed_w) {
-  int K = x.shape(-1);
-  int M = x.shape(-2);
-  int N = out.shape(-1);
-
-  int w_els = w.shape(-1) * w.shape(-2);
-  int g_els = scales.shape(-1) * scales.shape(-2);
-
-  auto out_ptr = out.data<T>();
-  auto x_ptr = x.data<T>();
-  auto w_ptr = w.data<uint32_t>();
-  auto scales_ptr = scales.data<uint8_t>();
-  auto lhs_indices_ptr = lhs_indices.data<uint32_t>();
-  auto rhs_indices_ptr = rhs_indices.data<uint32_t>();
-
-  for (int i = 0; i < lhs_indices.size(); i++) {
-    int x_idx = lhs_indices_ptr[elem_to_loc(
-        i, lhs_indices.shape(), lhs_indices.strides())];
-    int w_idx = rhs_indices_ptr[elem_to_loc(
-        i, rhs_indices.shape(), rhs_indices.strides())];
-    mxfp4_qmm_dispatch_transpose<T>(
-        out_ptr + i * M * N,
-        x_ptr + elem_to_loc(x_idx * M * K, x.shape(), x.strides()),
-        w_ptr + elem_to_loc(w_idx * w_els, w.shape(), w.strides()),
-        scales_ptr +
-            elem_to_loc(w_idx * g_els, scales.shape(), scales.strides()),
-        M,
-        N,
-        K,
-        transposed_w);
-  }
-}
-
-void mxfp4_bs_qmm_dispatch(
-    array& out,
-    const array& x,
-    const array& w,
-    const array& scales,
-    const array& lhs_indices,
-    const array& rhs_indices,
-    bool transposed_w) {
-  switch (x.dtype()) {
-    case float32:
-      mxfp4_bs_qmm_dispatch_typed<float>(
-          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
-      break;
-    case float16:
-      mxfp4_bs_qmm_dispatch_typed<float16_t>(
-          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
-      break;
-    case bfloat16:
-      mxfp4_bs_qmm_dispatch_typed<bfloat16_t>(
-          out, x, w, scales, lhs_indices, rhs_indices, transposed_w);
-      break;
-    default:
-      throw std::invalid_argument(
-          "[quantized_matmul] only floating types are supported");
-  }
-}
-
 } // namespace

 void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 4);
+
  auto& x_pre = inputs[0];
  auto& w_pre = inputs[1];
  auto& scales_pre = inputs[2];
+  auto& biases_pre = inputs[3];

-  auto& encoder = cpu::get_command_encoder(stream());
-  auto ensure_row_contiguous = [s = stream(), &encoder](const array& arr) {
+  std::vector<array> temps;
+  auto ensure_row_contiguous = [s = stream(), &temps](const array& arr) {
    if (arr.flags().row_contiguous) {
      return arr;
    } else {
-      auto arr_cpy = array(arr.shape(), arr.dtype(), nullptr, {});
-      copy_cpu(arr, arr_cpy, CopyType::General, s);
-      encoder.add_temporary(arr_cpy);
-      return arr_cpy;
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
+      copy_cpu(arr, temps.back(), CopyType::General, s);
+      return temps.back();
    }
  };

  auto x = ensure_row_contiguous(x_pre);
  auto w = ensure_row_contiguous(w_pre);
  auto scales = ensure_row_contiguous(scales_pre);
+  auto biases = ensure_row_contiguous(biases_pre);

  out.set_data(allocator::malloc(out.nbytes()));

+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.add_temporaries(std::move(temps));
  encoder.set_input_array(x);
  encoder.set_input_array(w);
  encoder.set_input_array(scales);
-  encoder.set_output_array(out);
-  if (mode_ == QuantizationMode::Affine) {
-    auto biases = ensure_row_contiguous(inputs[3]);
  encoder.set_input_array(biases);
+  encoder.set_output_array(out);
  encoder.dispatch([out = array::unsafe_weak_copy(out),
                    x = array::unsafe_weak_copy(x),
                    w = array::unsafe_weak_copy(w),
@@ -875,54 +558,48 @@ void QuantizedMatmul::eval_cpu(const std::vector<array>& inputs, array& out) {
                    transpose_ = transpose_]() mutable {
    _qmm_dispatch(out, x, w, scales, biases, group_size_, bits_, transpose_);
  });
-  } else {
-    encoder.dispatch([out = array::unsafe_weak_copy(out),
-                      x = array::unsafe_weak_copy(x),
-                      w = array::unsafe_weak_copy(w),
-                      scales = array::unsafe_weak_copy(scales),
-                      transpose_ = transpose_]() mutable {
-      mxfp4_qmm_dispatch(out, x, w, scales, transpose_);
-    });
-  }
 }

 void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
+  assert(inputs.size() == 6);
+
  auto& x_pre = inputs[0];
  auto& w_pre = inputs[1];
  auto& scales_pre = inputs[2];
-  auto& lhs_indices = inputs[inputs.size() - 2];
-  auto& rhs_indices = inputs[inputs.size() - 1];
+  auto& biases_pre = inputs[3];
+  auto& lhs_indices = inputs[4];
+  auto& rhs_indices = inputs[5];

-  auto& encoder = cpu::get_command_encoder(stream());
+  std::vector<array> temps;
  auto ensure_row_contiguous_last_dims = [s = stream(),
-                                          &encoder](const array& arr) {
+                                          &temps](const array& arr) {
    auto stride_0 = arr.strides()[arr.ndim() - 2];
    auto stride_1 = arr.strides()[arr.ndim() - 1];
    if (stride_0 == arr.shape(-1) && stride_1 == 1) {
      return arr;
    } else {
-      auto arr_cpy = array(arr.shape(), arr.dtype(), nullptr, {});
-      copy_cpu(arr, arr_cpy, CopyType::General, s);
-      encoder.add_temporary(arr_cpy);
-      return arr_cpy;
+      temps.push_back(array(arr.shape(), arr.dtype(), nullptr, {}));
+      copy_cpu(arr, temps.back(), CopyType::General, s);
+      return temps.back();
    }
  };

  auto x = ensure_row_contiguous_last_dims(x_pre);
  auto w = ensure_row_contiguous_last_dims(w_pre);
  auto scales = ensure_row_contiguous_last_dims(scales_pre);
+  auto biases = ensure_row_contiguous_last_dims(biases_pre);

  out.set_data(allocator::malloc(out.nbytes()));

+  auto& encoder = cpu::get_command_encoder(stream());
+  encoder.add_temporaries(std::move(temps));
  encoder.set_input_array(x);
  encoder.set_input_array(w);
  encoder.set_input_array(scales);
+  encoder.set_input_array(biases);
  encoder.set_input_array(lhs_indices);
  encoder.set_input_array(rhs_indices);
  encoder.set_output_array(out);
-  if (mode_ == QuantizationMode::Affine) {
-    auto biases = ensure_row_contiguous_last_dims(inputs[3]);
-    encoder.set_input_array(biases);
  encoder.dispatch([out = array::unsafe_weak_copy(out),
                    x = array::unsafe_weak_copy(x),
                    w = array::unsafe_weak_copy(w),
@@ -945,18 +622,6 @@ void GatherQMM::eval_cpu(const std::vector<array>& inputs, array& out) {
        bits_,
        transpose_);
  });
-  } else {
-    encoder.dispatch([out = array::unsafe_weak_copy(out),
-                      x = array::unsafe_weak_copy(x),
-                      w = array::unsafe_weak_copy(w),
-                      scales = array::unsafe_weak_copy(scales),
-                      lhs_indices = array::unsafe_weak_copy(lhs_indices),
-                      rhs_indices = array::unsafe_weak_copy(rhs_indices),
-                      transpose_ = transpose_]() mutable {
-      mxfp4_bs_qmm_dispatch(
-          out, x, w, scales, lhs_indices, rhs_indices, transpose_);
-    });
-  }
 }

 template <typename T, typename U>
@@ -1040,7 +705,7 @@ void dispatch_quantize(
      w_ptr, out_ptr, scales_ptr, biases_ptr, bits, group_size, w.size());
 }

-void fast::Quantize::eval_cpu(
+void fast::AffineQuantize::eval_cpu(
    const std::vector<array>& inputs,
    std::vector<array>& outputs) {
  auto ensure_row_contiguous = [s = stream()](const array& arr) {
@@ -1099,7 +764,7 @@ void fast::Quantize::eval_cpu(
      }
    } else {
      throw std::runtime_error(
-          "[fast::Quantize::eval_cpu] Only supports floating point inputs");
+          "[fast::AffineQuantize::eval_cpu] Only supports floating point inputs");
    }
  });
 }
--- a/mlx/backend/cpu/reduce.cpp
+++ b/mlx/backend/cpu/reduce.cpp
@@ -491,27 +491,19 @@ 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:
--- a/mlx/backend/cpu/simd/accelerate_simd.h
+++ b/mlx/backend/cpu/simd/accelerate_simd.h
@@ -234,7 +234,6 @@ Simd<T, N> remainder(Simd<T, N> a, Simd<T, N> b) {

 template <typename MaskT, typename T1, typename T2, int N>
 Simd<T1, N> select(Simd<MaskT, N> mask, Simd<T1, N> x, Simd<T2, N> y) {
-  static_assert(std::is_same_v<MaskT, bool>);
  if constexpr (sizeof(T1) == 1) {
    return asd::bitselect(y.value, x.value, asd::convert<char>(mask.value));
  } else if constexpr (sizeof(T1) == 2) {
@@ -252,13 +251,9 @@ Simd<T, N> pow(Simd<T, N> base, Simd<T, N> exp) {
    return asd::pow(base.value, exp.value);
  } else {
    Simd<T, N> res = 1;
-    // Raising an integer to a negative power is undefined
-    if (any(exp < 0)) {
-      return 0;
-    }
-    while (any(exp > 0)) {
-      res = select((exp & 1) != 0, res * base, res);
-      base = select(exp > 0, base * base, base);
+    while (any(exp)) {
+      res = select(exp & 1, res * base, res);
+      base = select(exp, base * base, base);
      exp = exp >> 1;
    }
    return res;
--- a/mlx/backend/cpu/sort.cpp
+++ b/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 {
@@ -333,23 +333,46 @@ 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]() mutable {
-    dispatch_all_types(out.dtype(), [&](auto type_tag) {
-      sort<MLX_GET_TYPE(type_tag)>(out, axis);
-    });
+  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_);
+        }
      });
 }

--- a/mlx/backend/cpu/svd.cpp
+++ b/mlx/backend/cpu/svd.cpp
@@ -81,7 +81,9 @@ void svd_impl(
    // Vᵀ of shape N x N. (M x M in lapack).
    const int ldvt = M;

-    auto jobz = (u_ptr) ? "A" : "N";
+    auto job_u = (u_ptr) ? "V" : "N";
+    auto job_vt = (u_ptr) ? "V" : "N";
+    static constexpr auto range = "A";

    // Will contain the number of singular values after the call has returned.
    int ns = 0;
@@ -89,20 +91,30 @@ 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) * 8 * K)};
+    auto iwork = array::Data{allocator::malloc(sizeof(int) * 12 * K)};

    static const int lwork_query = -1;

+    static const int ignored_int = 0;
+    static const T ignored_float = 0;
+
    int info;

    // Compute workspace size.
-    gesdd<T>(
-        /* jobz = */ jobz,
+    gesvdx<T>(
+        /* jobu = */ job_u,
+        /* jobvt = */ job_vt,
+        /* range = */ range,
        // 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,
@@ -124,13 +136,20 @@ void svd_impl(

    // Loop over matrices.
    for (int i = 0; i < num_matrices; i++) {
-      gesdd<T>(
-          /* jobz = */ jobz,
+      gesvdx<T>(
+          /* jobu = */ job_u,
+          /* jobvt = */ job_vt,
+          /* range = */ range,
          // 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,
@@ -148,6 +167,13 @@ 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);
--- a/mlx/backend/cuda/CMakeLists.txt
+++ b/mlx/backend/cuda/CMakeLists.txt
@@ -8,6 +8,7 @@ target_sources(
  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
@@ -16,13 +17,8 @@ target_sources(
          ${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}/distributed.cu
          ${CMAKE_CURRENT_SOURCE_DIR}/eval.cpp
          ${CMAKE_CURRENT_SOURCE_DIR}/event.cu
          ${CMAKE_CURRENT_SOURCE_DIR}/fence.cpp
@@ -43,26 +39,22 @@ 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/affine_quantize.cu
-          ${CMAKE_CURRENT_SOURCE_DIR}/quantized/quantized.cpp
+          ${CMAKE_CURRENT_SOURCE_DIR}/quantized.cu
          ${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)
+    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_batched_gemm_12_9.cu)
 else()
  target_sources(
-    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_gemm_batched_12_0.cpp)
+    mlx PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/gemms/cublas_batched_gemm_12_0.cpp)
 endif()

 target_compile_definitions(mlx PRIVATE MLX_USE_CUDA)
@@ -154,7 +146,7 @@ target_link_libraries(mlx PRIVATE CUDA::nvrtc CUDA::cuda_driver)
 FetchContent_Declare(
  cudnn
  GIT_REPOSITORY https://github.com/NVIDIA/cudnn-frontend.git
-  GIT_TAG v1.14.0
+  GIT_TAG v1.12.1
  GIT_SHALLOW TRUE
  EXCLUDE_FROM_ALL)
 set(CUDNN_FRONTEND_SKIP_JSON_LIB ON)
--- a/mlx/backend/cuda/allocator.cpp
+++ b/mlx/backend/cuda/allocator.cpp
@@ -30,15 +30,8 @@ SmallSizePool::SmallSizePool() {
  next_free_ = buffer_;

  CHECK_CUDA_ERROR(cudaMallocManaged(&data_, small_pool_size));
-#if CUDART_VERSION >= 13000
-  cudaMemLocation loc;
-  loc.type = cudaMemLocationTypeDevice;
-  loc.id = 0;
-#else
-  int loc = 0;
-#endif // CUDART_VERSION >= 13000
  CHECK_CUDA_ERROR(
-      cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetReadMostly, loc));
+      cudaMemAdvise(data_, small_pool_size, cudaMemAdviseSetReadMostly, 0));

  auto curr = next_free_;
  for (size_t i = 1; i < num_blocks; ++i) {
--- a/mlx/backend/cuda/arange.cu
+++ b/mlx/backend/cuda/arange.cu
@@ -6,33 +6,23 @@
 #include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"

-#include <cooperative_groups.h>
 #include <nvtx3/nvtx3.hpp>
+#include <thrust/device_ptr.h>
+#include <thrust/transform.h>

 namespace mlx::core {

 namespace cu {

-namespace cg = cooperative_groups;
+template <typename T>
+struct Arange {
+  const T start;
+  const T step;

-template <typename T, typename IdxT, int N_WRITES>
-__global__ void arange(T* out, IdxT size, T start, T step) {
-  IdxT index = cg::this_grid().thread_rank();
-
-  if ((index + 1) * N_WRITES > size) {
-    for (IdxT i = index * N_WRITES; i < size; ++i) {
-      out[i] = start + i * step;
-    }
-  } else {
-    AlignedVector<T, N_WRITES> out_vec;
-#pragma unroll
-    for (int i = 0; i < N_WRITES; ++i) {
-      out_vec[i] = start + (index * N_WRITES + i) * step;
-    }
-
-    store_vector<N_WRITES>(out, index, out_vec);
-  }
+  __device__ T operator()(uint32_t i) const {
+    return start + i * step;
  }
+};

 } // namespace cu

@@ -46,23 +36,19 @@ void Arange::eval_gpu(const std::vector<array>& inputs, array& out) {
  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>;
-    constexpr int N_WRITES = 16 / sizeof(OutType);
-    dispatch_bool(out.data_size() > INT32_MAX, [&](auto large) {
-      using IdxT = std::conditional_t<large(), int64_t, int32_t>;
-      auto [num_blocks, block_dims] = get_launch_args(out, large(), N_WRITES);
-      encoder.add_kernel_node(
-          cu::arange<OutType, IdxT, N_WRITES>,
-          num_blocks,
-          block_dims,
-          0,
-          out.data<OutType>(),
-          out.data_size(),
-          static_cast<CTYPE>(start_),
-          static_cast<CTYPE>(start_ + step_) - static_cast<CTYPE>(start_));
-    });
+    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)});
  });
 }

--- a/mlx/backend/cuda/arg_reduce.cu
+++ b/mlx/backend/cuda/arg_reduce.cu
@@ -44,11 +44,8 @@ struct ArgMin {
  }

  template <int N>
-  __device__ IndexValPair<T> reduce_many(
-      IndexValPair<T> best,
-      const AlignedVector<T, N>& vals,
-      uint32_t offset) {
-#pragma unroll
+  __device__ IndexValPair<T>
+  reduce_many(IndexValPair<T> best, T (&vals)[N], uint32_t offset) {
    for (int i = 0; i < N; i++) {
      if (vals[i] < best.val) {
        best.val = vals[i];
@@ -77,11 +74,8 @@ struct ArgMax {
  }

  template <int N>
-  __device__ IndexValPair<T> reduce_many(
-      IndexValPair<T> best,
-      const AlignedVector<T, N>& vals,
-      uint32_t offset) {
-#pragma unroll
+  __device__ IndexValPair<T>
+  reduce_many(IndexValPair<T> best, T (&vals)[N], uint32_t offset) {
    for (int i = 0; i < N; i++) {
      if (vals[i] > best.val) {
        best.val = vals[i];
@@ -112,15 +106,16 @@ __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;
-    auto vals = load_vector<N_READS>(in, tid, axis_size, axis_stride, init);
+    cub::LoadDirectBlocked(
+        tid, StridedIterator(in + in_idx, axis_stride), vals, axis_size, init);
    best = op.reduce_many(best, vals, tid * N_READS);
  }

@@ -171,7 +166,6 @@ 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(),
--- a/mlx/backend/cuda/binary/binary.cuh
+++ b/mlx/backend/cuda/binary/binary.cuh
@@ -99,89 +99,39 @@ __global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
  }
 }

-template <
-    typename Op,
-    typename In,
-    typename Out,
-    typename IdxT,
-    int NDIM,
-    int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
 __global__ void binary_g_nd(
    const In* a,
    const In* b,
    Out* out,
-    IdxT size_rest,
+    IdxT size,
    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) {
-  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;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
    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]);
+        index, shape.data(), a_strides.data(), b_strides.data());
+    out[index] = Op{}(a[a_idx], b[b_idx]);
  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }

-template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_g(
    const In* a,
    const In* b,
    Out* out,
-    IdxT size_rest,
+    IdxT size,
    const __grid_constant__ Shape shape,
    const __grid_constant__ Strides a_strides,
    const __grid_constant__ Strides b_strides,
    int ndim) {
-  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;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
    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]);
+        index, shape.data(), a_strides.data(), b_strides.data(), ndim);
+    out[index] = Op{}(a[a_idx], b[b_idx]);
  }
-  store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
 }

 template <typename Op, typename In, typename Out>
@@ -259,61 +209,37 @@ 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(),
-                        1>;
-                    if (work_per_thread == 4) {
-                      kernel = cu::binary_g_nd<
-                          Op,
-                          InType,
-                          OutType,
-                          IdxT,
-                          dims_constant(),
-                          4>;
-                    }
+                    auto [num_blocks, block_dims] =
+                        get_launch_args(out, large());
                    encoder.add_kernel_node(
-                        kernel,
-                        {num_blocks_x, num_blocks_y},
+                        cu::binary_g_nd<
+                            Op,
+                            InType,
+                            OutType,
+                            IdxT,
+                            dims_constant()>,
+                        num_blocks,
                        block_dims,
-                        0,
                        a.data<InType>(),
                        b.data<InType>(),
                        out.data<OutType>(),
-                        rest,
+                        out.size(),
                        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, 1>;
-                  if (work_per_thread == 4) {
-                    kernel = cu::binary_g<Op, InType, OutType, IdxT, 4>;
-                  }
+                  auto [num_blocks, block_dims] = get_launch_args(out, large());
                  encoder.add_kernel_node(
-                      kernel,
-                      {num_blocks_x, num_blocks_y},
+                      cu::binary_g<Op, InType, OutType, IdxT>,
+                      num_blocks,
                      block_dims,
-                      0,
                      a.data<InType>(),
                      b.data<InType>(),
                      out.data<OutType>(),
-                      rest,
+                      out.size(),
                      const_param(shape),
                      const_param(a_strides),
                      const_param(b_strides),
@@ -338,7 +264,6 @@ void binary_op_gpu_inplace(
                kernel,
                num_blocks,
                block_dims,
-                0,
                a.data<InType>(),
                b.data<InType>(),
                out.data<OutType>(),
@@ -376,4 +301,54 @@ 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
--- a/mlx/backend/cuda/binary/CMakeLists.txt
+++ b/mlx/backend/cuda/binary/CMakeLists.txt
@@ -1,21 +0,0 @@
-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)
--- a/mlx/backend/cuda/binary/add.cu
+++ b/mlx/backend/cuda/binary/add.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Add)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/arctan2.cu
+++ b/mlx/backend/cuda/binary/arctan2.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(ArcTan2)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/bitwise_binary.cu
+++ b/mlx/backend/cuda/binary/bitwise_binary.cu
@@ -1,27 +0,0 @@
-// 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
--- a/mlx/backend/cuda/binary/divide.cu
+++ b/mlx/backend/cuda/binary/divide.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Divide)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/equal.cu
+++ b/mlx/backend/cuda/binary/equal.cu
@@ -1,15 +0,0 @@
-// 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
--- a/mlx/backend/cuda/binary/greater.cu
+++ b/mlx/backend/cuda/binary/greater.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Greater)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/greater_equal.cu
+++ b/mlx/backend/cuda/binary/greater_equal.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(GreaterEqual)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/less.cu
+++ b/mlx/backend/cuda/binary/less.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Less)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/less_equal.cu
+++ b/mlx/backend/cuda/binary/less_equal.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(LessEqual)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/log_add_exp.cu
+++ b/mlx/backend/cuda/binary/log_add_exp.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(LogAddExp)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/logical_and.cu
+++ b/mlx/backend/cuda/binary/logical_and.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(LogicalAnd)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/logical_or.cu
+++ b/mlx/backend/cuda/binary/logical_or.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(LogicalOr)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/maximum.cu
+++ b/mlx/backend/cuda/binary/maximum.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Maximum)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/minimum.cu
+++ b/mlx/backend/cuda/binary/minimum.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Minimum)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/multiply.cu
+++ b/mlx/backend/cuda/binary/multiply.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Multiply)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/not_equal.cu
+++ b/mlx/backend/cuda/binary/not_equal.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(NotEqual)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/power.cu
+++ b/mlx/backend/cuda/binary/power.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Power)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/remainder.cu
+++ b/mlx/backend/cuda/binary/remainder.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Remainder)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary/subtract.cu
+++ b/mlx/backend/cuda/binary/subtract.cu
@@ -1,7 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/binary/binary.cuh"
-
-namespace mlx::core {
-BINARY_GPU(Subtract)
-} // namespace mlx::core
--- a/mlx/backend/cuda/binary_two.cu
+++ b/mlx/backend/cuda/binary_two.cu
@@ -127,99 +127,45 @@ 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,
-    int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
 __global__ void binary_two_g_nd(
    const In* a,
    const In* b,
    Out* out_a,
    Out* out_b,
-    IdxT size_rest,
+    IdxT size,
    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) {
-  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;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
    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];
+        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];
  }
-  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, int N_READS>
+template <typename Op, typename In, typename Out, typename IdxT>
 __global__ void binary_two_g(
    const In* a,
    const In* b,
    Out* out_a,
    Out* out_b,
-    IdxT size_rest,
+    IdxT size,
    const __grid_constant__ Shape shape,
    const __grid_constant__ Strides a_strides,
    const __grid_constant__ Strides b_strides,
    int ndim) {
-  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;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
    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];
+        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];
  }
-  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>
@@ -279,64 +225,40 @@ 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<
-                        Op,
-                        InType,
-                        OutType,
-                        IdxT,
-                        dims_constant(),
-                        1>;
-                    if (work_per_thread == 4) {
-                      kernel = cu::binary_two_g_nd<
-                          Op,
-                          InType,
-                          OutType,
-                          IdxT,
-                          dims_constant(),
-                          4>;
-                    }
+                    auto [num_blocks, block_dims] =
+                        get_launch_args(out_a, large());
                    encoder.add_kernel_node(
-                        kernel,
-                        {num_blocks_x, num_blocks_y},
+                        cu::binary_two_g_nd<
+                            Op,
+                            InType,
+                            OutType,
+                            IdxT,
+                            dims_constant()>,
+                        num_blocks,
                        block_dims,
-                        0,
                        a.data<InType>(),
                        b.data<InType>(),
                        out_a.data<OutType>(),
                        out_b.data<OutType>(),
-                        rest,
+                        out_a.size(),
                        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, 1>;
-                  if (work_per_thread == 4) {
-                    kernel = cu::binary_two_g<Op, InType, OutType, IdxT, 4>;
-                  }
+                  auto [num_blocks, block_dims] =
+                      get_launch_args(out_a, large());
                  encoder.add_kernel_node(
-                      kernel,
-                      {num_blocks_x, num_blocks_y},
+                      cu::binary_two_g<Op, InType, OutType, IdxT>,
+                      num_blocks,
                      block_dims,
-                      0,
                      a.data<InType>(),
                      b.data<InType>(),
                      out_a.data<OutType>(),
                      out_b.data<OutType>(),
-                      rest,
+                      out_a.size(),
                      const_param(shape),
                      const_param(a_strides),
                      const_param(b_strides),
@@ -365,7 +287,6 @@ void binary_two_op_gpu_inplace(
                kernel,
                num_blocks,
                block_dims,
-                0,
                a.data<InType>(),
                b.data<InType>(),
                out_a.data<OutType>(),
--- a/mlx/backend/cuda/compiled.cpp
+++ b/mlx/backend/cuda/compiled.cpp
@@ -104,41 +104,10 @@ 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
-    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) {
@@ -153,7 +122,7 @@ struct FusedKernelBuilder {
      } else if (is_scalar(x)) {
        value = fmt::format("{}[0]", xname);
      } else if (contiguous) {
-        value = fmt::format("vec_{}[i]", xname);
+        value = fmt::format("{}[index]", xname);
      } else {
        value = fmt::format("{}[{}_idx]", xname, xname);
      }
@@ -181,30 +150,25 @@ struct FusedKernelBuilder {

    // Write output.
    for (const auto& x : outputs) {
-      os += fmt::format("    vec_{0}[i] = tmp_{0};\n", namer.get_name(x));
+      os += fmt::format("    {0}[index] = 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 += fmt::format("    {0}_idx += {0}_strides[NDIM - 1];\n", xname);
+        os += "    " + xname + "_idx += " + xname + "_strides[NDIM - 1];\n";
      }
    }
    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";
  }
 };
@@ -228,15 +192,6 @@ 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{
@@ -250,6 +205,7 @@ 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(),
@@ -258,17 +214,20 @@ void Compiled::eval_gpu(
          "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, wpt));
+            "mlx::core::cu::{}_strided<{}, uint32_t, {}>",
+            lib_name(),
+            i,
+            work_per_thread));
        kernel_names.push_back(fmt::format(
-            "mlx::core::cu::{}_strided<{}, int64_t, {}>", lib_name(), i, wpt));
+            "mlx::core::cu::{}_strided<{}, int64_t, {}>",
+            lib_name(),
+            i,
+            work_per_thread));
      }
    }
-
-    return std::make_tuple(
-        false, std::move(builder.os), std::move(kernel_names));
+    return std::make_pair(std::move(builder.os), std::move(kernel_names));
  });

  // Collapse contiguous dims to route to a faster kernel if possible. Also
@@ -310,6 +269,7 @@ 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;
  }
@@ -335,7 +295,7 @@ void Compiled::eval_gpu(
  auto kernel = mod.get_kernel(kernel_name);
  auto [num_blocks, block_dims] =
      get_launch_args(outputs[0], large, work_per_thread);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }

 } // namespace mlx::core
--- a/mlx/backend/cuda/conv.cpp
+++ b/mlx/backend/cuda/conv.cpp
@@ -1,214 +1,257 @@
 // 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/device/config.h"
 #include "mlx/backend/cuda/lru_cache.h"
 #include "mlx/backend/gpu/copy.h"
+#include "mlx/dtype_utils.h"
 #include "mlx/primitives.h"

+// cudnn_frontend.h redefines this macro.
+#undef CHECK_CUDA_ERROR
+
+#include <cudnn_frontend.h>
+#include <cudnn_frontend_find_plan.h>
+#include <fmt/format.h>
 #include <nvtx3/nvtx3.hpp>

 #include <cassert>
+#include <numeric>

 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)
+// Not all engines support it so can not use this API now.
+#define MLX_USE_CUDNN_NATIVE_CUDA_GRAPH_API 0

 struct ConvCacheKey {
  int device_id;
-  cudnnDataType_t cudnn_dtype;
+  cudnnBackendDescriptorType_t backend_type;
+  cudnnDataType_t cudnn_type;
  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> filter_shape;
  std::array<int, MAX_NDIM> padding_lo;
  std::array<int, MAX_NDIM> padding_hi;
+  std::array<int, MAX_NDIM> stride;
  std::array<int, MAX_NDIM> dilation;
  int groups;
-  bool flip;
  uint8_t input_alignment;
-  uint8_t weight_alignment;
+  uint8_t filter_alignment;
  uint8_t output_alignment;
 };

 auto& conv_cache() {
-  static LRUBytesKeyCache<
-      ConvCacheKey,
-      std::pair<
-          cudnnBackendDescriptorType_t,
-          std::optional<cudnn_frontend::ExecutionPlan>>>
-      cache(/* capacity */ 128);
+  static LRUBytesKeyCache<ConvCacheKey, 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];
+template <typename T, typename U>
+inline std::vector<T> convert_vector(const std::vector<U>& vec) {
+  return std::vector<T>(vec.begin(), vec.end());
 }
-    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));
+template <typename T>
+inline std::array<T, MAX_NDIM> fixed_vector(const std::vector<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;
+}

-  } 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));
+auto nhwc_to_nchw(const array& x) {
+  auto shape = convert_vector<int64_t>(x.shape());
+  shape.insert(shape.begin() + 1, shape.back());
+  shape.erase(shape.end() - 1);
+  auto strides = convert_vector<int64_t>(x.strides());
+  strides.insert(strides.begin() + 1, strides.back());
+  strides.erase(strides.end() - 1);
+  return std::make_tuple(shape, strides);
+}
+
+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)));
  }
 }

-std::optional<cudnn_frontend::OperationGraph> build_conv_op_graph(
-    cu::CommandEncoder& encoder,
+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;
+}
+
+inline cudnn_frontend::Tensor build_tensor(int64_t id, const array& x) {
+  auto [shape, strides] = nhwc_to_nchw(x);
+  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();
+}
+
+cudnn_frontend::EngineConfigList get_engine_configs(
    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())
+    cudnn_frontend::OperationGraph& op_graph,
+    bool use_fallback = false) {
+  cudnn_frontend::GeneratorSource source;
+  if (use_fallback) {
+    source = [&backend_type](cudnn_frontend::OperationGraph& op_graph) {
+      auto fallback = cudnn_frontend::EngineFallbackListBuilder()
+                          .setOperationGraph(op_graph)
+                          .setOperation(backend_type)
                          .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)
+      return fallback.getFallbackList();
+    };
+  } else {
+    source = [](cudnn_frontend::OperationGraph& op_graph) {
+      auto heuristics = cudnn_frontend::EngineHeuristicsBuilder()
+                            .setOperationGraph(op_graph)
+                            .setHeurMode(CUDNN_HEUR_MODE_A)
                            .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;
-  }
+      return heuristics.getEngineConfig(heuristics.getEngineConfigCount());
+    };
  }

-// 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);
+  cudnn_frontend::EngineConfigGenerator generator(1, &source);
+  auto configs = generator.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;
    }
-  int ndim = x.ndim();
-  if (group_dim < 0) {
-    group_dim += ndim;
+    if (cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_TENSOR_CORE>(c) &&
+        dtype == float32 && !env::enable_tf32()) {
+      return true;
    }
-  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;
+    return false;
+  });
+  return filtered_configs;
 }

-// 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(
+bool execute_plan(
    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];
+    cudnn_frontend::ExecutionPlan& plan,
+    const array& in,
+    const array& wt,
+    array& out) {
+  int workspace_size = plan.getWorkspaceSize();
+  array workspace(allocator::malloc(workspace_size), {workspace_size}, uint8);
+
+  int64_t uids[3] = {'x', 'w', 'y'};
+  void* data_ptrs[3] = {
+      const_cast<void*>(in.data<void>()),
+      const_cast<void*>(wt.data<void>()),
+      out.data<void>(),
+  };
+
+  auto variantPack = cudnn_frontend::VariantPackBuilder()
+                         .setWorkspacePointer(workspace.data<void>())
+                         .setDataPointers(3, data_ptrs)
+                         .setUids(3, uids)
+                         .build();
+
+  auto handle = encoder.device().cudnn_handle();
+  cudnnSetStream(handle, encoder.stream());
+
+#if CUDNN_VERSION >= 90500 && MLX_USE_CUDNN_NATIVE_CUDA_GRAPH_API
+  cudaGraph_t graph;
+  cudaGraphCreate(&graph, 0);
+  std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
+      &graph, [](cudaGraph_t* p) { cudaGraphDestroy(*p); });
+  if (cudnnBackendPopulateCudaGraph(
+          handle, plan.get_raw_desc(), variantPack.get_raw_desc(), graph) !=
+      CUDNN_STATUS_SUCCESS) {
+    return false;
  }
-    array intermediate(std::move(shape), out.dtype(), nullptr, {});
-    intermediate.copy_shared_buffer(
-        out, std::move(strides), {true, true, false}, out.data_size());
-    out = intermediate;
+  encoder.add_graph_node(graph);
+#else
+  auto capture = encoder.capture_context();
+  if (cudnnBackendExecute(
+          handle, plan.get_raw_desc(), variantPack.get_raw_desc()) !=
+      CUDNN_STATUS_SUCCESS) {
+    // Discard the captured graph when failed.
+    capture.discard = true;
+    return false;
  }
+#endif
+
+  encoder.add_temporary(workspace);
+  return true;
+}
+
+bool try_engines(
+    cu::CommandEncoder& encoder,
+    cudnn_frontend::EngineConfigList& configs,
+    const ConvCacheKey& cache_key,
+    const std::string& op_graph_tag,
+    const array& in,
+    const array& wt,
+    array& out) {
+  for (auto& config : configs) {
+    try {
+      auto plan = cudnn_frontend::ExecutionPlanBuilder()
+                      .setHandle(encoder.device().cudnn_handle())
+                      .setEngineConfig(config, op_graph_tag)
+                      .build();
+      if (execute_plan(encoder, plan, in, wt, out)) {
+        conv_cache().emplace(cache_key, std::move(plan));
+        return true;
+      }
+    } catch (cudnn_frontend::cudnnException&) {
+    }
+  }
+  return false;
+}
+
+} // 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];
+  out.set_data(allocator::malloc(out.nbytes()));
+
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);

  // cuDNN requires contiguous input.
+  // TODO: Handle NCHW format specially.
  if (!in.flags().row_contiguous) {
    in = contiguous_copy_gpu(in, s);
    encoder.add_temporary(in);
@@ -218,201 +261,80 @@ std::tuple<array, array, array> prepare_args(
    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);
+  encoder.set_output_array(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);
+  auto backend_type = CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR;
+  auto cudnn_type = dtype_to_cudnn_type(in.dtype());

  // 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_),
+      backend_type,
+      cudnn_type,
+      fixed_vector(in.shape()),
+      fixed_vector(wt.shape()),
+      fixed_vector(padding_lo_),
+      fixed_vector(padding_hi_),
+      fixed_vector(kernel_strides_),
+      fixed_vector(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);
+    if (!execute_plan(encoder, it->second, in, wt, out)) {
+      throw std::runtime_error("Cached convolution plan failed to execute.");
    }
    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};
-    }
-  }
+  // Build operation graph.
+  auto compute_data_type = (in.dtype() == float16 || in.dtype() == bfloat16)
+      ? CUDNN_DATA_FLOAT
+      : cudnn_type;

-  // 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;
-    }
-  }
+  auto stride = convert_vector<int64_t>(kernel_strides_);
+  auto padding_lo = convert_vector<int64_t>(padding_lo_);
+  auto padding_hi = convert_vector<int64_t>(padding_hi_);
+  auto dilation = convert_vector<int64_t>(kernel_dilation_);

-  if (op_graph) {
-    // Setup inputs and outputs.
-    register_args(encoder, backend_type, in, wt, out, out_);
+  auto conv_desc = cudnn_frontend::ConvDescBuilder()
+                       .setDataType(compute_data_type)
+                       .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();

-    // 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)));
+  auto op = cudnn_frontend::OperationBuilder(backend_type)
+                .setxDesc(build_tensor('x', in))
+                .setwDesc(build_tensor('w', wt))
+                .setyDesc(build_tensor('y', out))
+                .setcDesc(conv_desc)
+                .build();
+
+  std::array<cudnn_frontend::Operation const*, 1> ops = {&op};
+  auto op_graph = cudnn_frontend::OperationGraphBuilder()
+                      .setHandle(encoder.device().cudnn_handle())
+                      .setOperationGraph(ops.size(), ops.data())
+                      .build();
+
+  // Try to run plans based on heuristics.
+  auto configs = get_engine_configs(backend_type, in.dtype(), op_graph);
+  auto op_graph_tag = op_graph.getTag();
+  if (try_engines(encoder, configs, cache_key, op_graph_tag, in, wt, out)) {
    return;
  }
+  // Then try fallback plans.
+  configs = get_engine_configs(backend_type, in.dtype(), op_graph);
+  if (try_engines(encoder, configs, cache_key, op_graph_tag, in, wt, out)) {
+    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));
+  throw std::runtime_error("Unable to find an engine for convolution.");
 }

 } // namespace mlx::core
--- a/mlx/backend/cuda/conv/conv.h
+++ b/mlx/backend/cuda/conv/conv.h
@@ -1,126 +0,0 @@
-// 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
--- a/mlx/backend/cuda/conv/gemm_conv.cu
+++ b/mlx/backend/cuda/conv/gemm_conv.cu
@@ -1,217 +0,0 @@
-// 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
--- a/mlx/backend/cuda/conv/gemm_grouped_conv.cu
+++ b/mlx/backend/cuda/conv/gemm_grouped_conv.cu
@@ -1,231 +0,0 @@
-// 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
--- a/mlx/backend/cuda/copy.cu
+++ b/mlx/backend/cuda/copy.cu
@@ -15,8 +15,8 @@ void copy_gpu_inplace(
    int64_t offset_out,
    CopyType ctype,
    const Stream& s,
-    std::optional<array> dynamic_offset_in,
-    std::optional<array> dynamic_offset_out) {
+    const std::optional<array>& dynamic_offset_in,
+    const std::optional<array>& dynamic_offset_out) {
  if (out.size() == 0) {
    return;
  }
@@ -44,16 +44,6 @@ void copy_gpu_inplace(
          strides_vec[0]);
    } else {
      if (dynamic_offset_in || dynamic_offset_out) {
-        if (!dynamic_offset_in) {
-          dynamic_offset_in = array(0, int64);
-          encoder.add_temporary(*dynamic_offset_in);
-        }
-        if (!dynamic_offset_out) {
-          dynamic_offset_out = array(0, int64);
-          encoder.add_temporary(*dynamic_offset_out);
-        }
-        encoder.set_input_array(*dynamic_offset_in);
-        encoder.set_input_array(*dynamic_offset_out);
        copy_general_dynamic(
            encoder,
            ctype,
@@ -64,8 +54,8 @@ void copy_gpu_inplace(
            shape_collapsed,
            strides_vec[0],
            strides_vec[1],
-            *dynamic_offset_in,
-            *dynamic_offset_out);
+            dynamic_offset_in ? *dynamic_offset_in : array(0, int64),
+            dynamic_offset_out ? *dynamic_offset_out : array(0, int64));
      } else {
        copy_general(
            encoder,
--- a/mlx/backend/cuda/copy/copy_contiguous.cu
+++ b/mlx/backend/cuda/copy/copy_contiguous.cu
@@ -76,7 +76,6 @@ void copy_contiguous(
            kernel,
            num_blocks,
            block_dims,
-            0,
            in.data<InType>() + in_offset,
            out.data<OutType>() + out_offset,
            out.data_size());
--- a/mlx/backend/cuda/copy/copy_general.cu
+++ b/mlx/backend/cuda/copy/copy_general.cu
@@ -10,80 +10,37 @@ namespace cu {

 namespace cg = cooperative_groups;

-template <typename In, typename Out, typename IdxT, int NDIM, int N_READS>
+template <typename In, typename Out, typename IdxT, int NDIM>
 __global__ void copy_gg_nd(
    const In* in,
    Out* out,
-    IdxT size_rest,
+    IdxT size,
    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) {
-  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;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
    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]);
+        index, shape.data(), strides_in.data(), strides_out.data());
+    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
  }
-  store_vector(out + idx_out, index_x, out_vec, shape_x, out_stride_x);
 }

-template <typename In, typename Out, typename IdxT, int N_READS>
+template <typename In, typename Out, typename IdxT>
 __global__ void copy_gg(
    const In* in,
    Out* out,
-    IdxT size_rest,
+    IdxT size,
    const __grid_constant__ Shape shape,
    const __grid_constant__ Strides strides_in,
    const __grid_constant__ Strides strides_out,
    int ndim) {
-  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;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
    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]);
+        index, shape.data(), strides_in.data(), strides_out.data(), ndim);
+    out[idx_out] = CastOp<In, Out>{}(in[idx_in]);
  }
-  store_vector(out + idx_out, index_x, out_vec, shape_x, out_stride_x);
 }

 } // namespace cu
@@ -112,52 +69,31 @@ 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(), 1>;
-                if (work_per_thread == 4) {
-                  kernel =
-                      cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant(), 4>;
-                }
+                auto [num_blocks, block_dims] =
+                    get_launch_args(data_size, shape, out.strides(), large());
                encoder.add_kernel_node(
-                    kernel,
-                    {num_blocks_x, num_blocks_y},
+                    cu::copy_gg_nd<InType, OutType, IdxT, ndim_constant()>,
+                    num_blocks,
                    block_dims,
-                    0,
                    in_ptr,
                    out_ptr,
-                    rest,
+                    data_size,
                    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, 1>;
-              if (work_per_thread == 4) {
-                kernel = cu::copy_gg<InType, OutType, IdxT, 4>;
-              }
+              auto [num_blocks, block_dims] =
+                  get_launch_args(data_size, shape, out.strides(), large());
              encoder.add_kernel_node(
-                  kernel,
-                  {num_blocks_x, num_blocks_y},
+                  cu::copy_gg<InType, OutType, IdxT>,
+                  num_blocks,
                  block_dims,
-                  0,
                  in_ptr,
                  out_ptr,
-                  rest,
+                  data_size,
                  const_param(shape),
                  const_param(strides_in),
                  const_param(strides_out),
--- a/mlx/backend/cuda/copy/copy_general_dynamic.cu
+++ b/mlx/backend/cuda/copy/copy_general_dynamic.cu
@@ -83,7 +83,6 @@ void copy_general_dynamic(
                        dims_constant()>,
                    num_blocks,
                    block_dims,
-                    0,
                    in_ptr,
                    out_ptr,
                    out.size(),
@@ -99,7 +98,6 @@ void copy_general_dynamic(
                  cu::copy_gg_dynamic<InType, OutType, IdxT>,
                  num_blocks,
                  block_dims,
-                  0,
                  in_ptr,
                  out_ptr,
                  out.size(),
--- a/mlx/backend/cuda/copy/copy_general_input.cu
+++ b/mlx/backend/cuda/copy/copy_general_input.cu
@@ -10,67 +10,33 @@ namespace cu {

 namespace cg = cooperative_groups;

-template <typename In, typename Out, typename IdxT, int NDIM, int N_READS>
+template <typename In, typename Out, typename IdxT, int NDIM>
 __global__ void copy_g_nd(
    const In* in,
    Out* out,
-    IdxT size_rest,
+    IdxT size,
    const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
-    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;
+    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]);
+  }
 }

-  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, int N_READS>
+template <typename In, typename Out, typename IdxT>
 __global__ void copy_g(
    const In* in,
    Out* out,
-    IdxT size_rest,
+    IdxT size,
    const __grid_constant__ Shape shape,
-    const __grid_constant__ Strides strides,
+    const __grid_constant__ Strides strides_in,
    int ndim) {
-  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;
+  IdxT index = cg::this_grid().thread_rank();
+  if (index < size) {
+    IdxT idx_in = elem_to_loc(index, shape.data(), strides_in.data(), ndim);
+    out[index] = CastOp<In, Out>{}(in[idx_in]);
  }
-
-  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
@@ -95,49 +61,28 @@ 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(), 1>;
-                if (work_per_thread == 4) {
-                  kernel =
-                      cu::copy_g_nd<InType, OutType, IdxT, dims_constant(), 4>;
-                }
+                auto [num_blocks, block_dims] = get_launch_args(out, large());
                encoder.add_kernel_node(
-                    kernel,
-                    {num_blocks_x, num_blocks_y},
+                    cu::copy_g_nd<InType, OutType, IdxT, dims_constant()>,
+                    num_blocks,
                    block_dims,
-                    0,
                    in_ptr,
                    out_ptr,
-                    rest,
+                    out.size(),
                    const_param<dims_constant()>(shape),
                    const_param<dims_constant()>(strides_in));
              });
            } else { // ndim >= 4
-              auto kernel = cu::copy_g<InType, OutType, IdxT, 1>;
-              if (work_per_thread == 4) {
-                kernel = cu::copy_g<InType, OutType, IdxT, 4>;
-              }
+              auto [num_blocks, block_dims] = get_launch_args(out, large());
              encoder.add_kernel_node(
-                  kernel,
-                  {num_blocks_x, num_blocks_y},
+                  cu::copy_g<InType, OutType, IdxT>,
+                  num_blocks,
                  block_dims,
-                  0,
                  in_ptr,
                  out_ptr,
-                  rest,
+                  out.size(),
                  const_param(shape),
                  const_param(strides_in),
                  ndim);
--- a/mlx/backend/cuda/cudnn_utils.cpp
+++ b/mlx/backend/cuda/cudnn_utils.cpp
@@ -1,272 +0,0 @@
-// 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
--- a/mlx/backend/cuda/cudnn_utils.h
+++ b/mlx/backend/cuda/cudnn_utils.h
@@ -1,164 +0,0 @@
-// 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
--- a/mlx/backend/cuda/custom_kernel.cpp
+++ b/mlx/backend/cuda/custom_kernel.cpp
@@ -1,379 +0,0 @@
-// 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
--- a/mlx/backend/cuda/device.cpp
+++ b/mlx/backend/cuda/device.cpp
@@ -29,18 +29,11 @@ void check_cudnn_error(const char* name, cudnnStatus_t err) {

 int cuda_graph_cache_size() {
  static int cache_size = []() {
-    return env::get_var("MLX_CUDA_GRAPH_CACHE_SIZE", 400);
+    return env::get_var("MLX_CUDA_GRAPH_CACHE_SIZE", 100);
  }();
  return cache_size;
 }

-bool use_cuda_graphs() {
-  static bool use_graphs = []() {
-    return env::get_var("MLX_USE_CUDA_GRAPHS", true);
-  }();
-  return use_graphs;
-}
-
 } // namespace

 Device::Device(int device) : device_(device) {
@@ -93,19 +86,14 @@ CommandEncoder& Device::get_command_encoder(Stream s) {

 CommandEncoder::CaptureContext::CaptureContext(CommandEncoder& enc) : enc(enc) {
  enc.device().make_current();
-  if (!use_cuda_graphs()) {
-    return;
-  }
  CHECK_CUDA_ERROR(
      cudaStreamBeginCapture(enc.stream(), cudaStreamCaptureModeGlobal));
 }

 CommandEncoder::CaptureContext::~CaptureContext() {
-  if (!use_cuda_graphs()) {
-    return;
-  }
-
-  graph.end_capture(enc.stream());
+  CHECK_CUDA_ERROR(cudaStreamEndCapture(enc.stream(), &graph));
+  std::unique_ptr<cudaGraph_t, void (*)(cudaGraph_t*)> graph_freer(
+      &graph, [](cudaGraph_t* p) { CHECK_CUDA_ERROR(cudaGraphDestroy(*p)); });
  if (discard) {
    return;
  }
@@ -119,9 +107,6 @@ CommandEncoder::ConcurrentContext::ConcurrentContext(CommandEncoder& enc)

 CommandEncoder::ConcurrentContext::~ConcurrentContext() {
  enc.in_concurrent_ = false;
-  if (!use_cuda_graphs()) {
-    return;
-  }

  // Use an empty graph node for synchronization
  CommandEncoder::GraphNode empty{NULL, 'E', std::to_string(enc.node_count_++)};
@@ -199,29 +184,31 @@ void CommandEncoder::insert_graph_dependencies(std::vector<GraphNode> nodes) {
  }
 }

-CommandEncoder::CommandEncoder(Device& d)
-    : device_(d),
-      stream_(d),
-      graph_(d),
-      graph_cache_(cuda_graph_cache_size()) {}
+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_);
+}

 void CommandEncoder::add_completed_handler(std::function<void()> task) {
  worker_.add_task(std::move(task));
 }

 void CommandEncoder::set_input_array(const array& arr) {
-  if (!use_cuda_graphs()) {
-    return;
-  }
  auto id = reinterpret_cast<std::uintptr_t>(arr.buffer().ptr());
  active_deps_.push_back(id);
 }

 void CommandEncoder::set_output_array(const array& arr) {
-  if (!use_cuda_graphs()) {
-    return;
-  }
-
  auto id = reinterpret_cast<std::uintptr_t>(arr.buffer().ptr());
  active_deps_.push_back(id);
  active_outputs_.push_back(id);
@@ -237,19 +224,12 @@ void CommandEncoder::add_kernel_node(
    void* func,
    dim3 grid_dim,
    dim3 block_dim,
-    uint32_t smem_bytes,
    void** params) {
-  if (!use_cuda_graphs()) {
-    CHECK_CUDA_ERROR(cudaLaunchKernel(
-        func, grid_dim, block_dim, params, smem_bytes, stream()));
-    return;
-  }
  cudaKernelNodeParams kernel_params = {0};
  kernel_params.func = func;
  kernel_params.gridDim = grid_dim;
  kernel_params.blockDim = block_dim;
  kernel_params.kernelParams = params;
-  kernel_params.sharedMemBytes = smem_bytes;
  add_kernel_node(kernel_params);
 }

@@ -257,24 +237,7 @@ void CommandEncoder::add_kernel_node(
    CUfunction func,
    dim3 grid_dim,
    dim3 block_dim,
-    uint32_t smem_bytes,
    void** params) {
-  if (!use_cuda_graphs()) {
-    CHECK_CUDA_ERROR(cuLaunchKernel(
-        func,
-        grid_dim.x,
-        grid_dim.y,
-        grid_dim.z,
-        block_dim.x,
-        block_dim.y,
-        block_dim.z,
-        smem_bytes,
-        stream(),
-        params,
-        nullptr));
-    return;
-  }
-
  CUDA_KERNEL_NODE_PARAMS kernel_params = {0};
  kernel_params.func = func;
  kernel_params.gridDimX = grid_dim.x;
@@ -284,7 +247,6 @@ void CommandEncoder::add_kernel_node(
  kernel_params.blockDimY = block_dim.y;
  kernel_params.blockDimZ = block_dim.z;
  kernel_params.kernelParams = params;
-  kernel_params.sharedMemBytes = smem_bytes;
  add_kernel_node(kernel_params);
 }

@@ -301,32 +263,19 @@ void CommandEncoder::add_kernel_node(const CUDA_KERNEL_NODE_PARAMS& params) {
 }

 void CommandEncoder::add_graph_node(cudaGraph_t child) {
-  if (!use_cuda_graphs()) {
-    CudaGraphExec graph_exec;
-    graph_exec.instantiate(child);
-    device_.make_current();
-    CHECK_CUDA_ERROR(cudaGraphLaunch(graph_exec, stream()));
-  }
  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_)]() {});
  }
  if (node_count_ > 0) {
    if (!from_nodes_.empty()) {
      CHECK_CUDA_ERROR(cudaGraphAddDependencies(
-          graph_,
-          from_nodes_.data(),
-          to_nodes_.data(),
-#if CUDART_VERSION >= 13000
-          nullptr, // edgeData
-#endif // CUDART_VERSION >= 13000
-          from_nodes_.size()));
+          graph_, from_nodes_.data(), to_nodes_.data(), from_nodes_.size()));
    }

    graph_key_ += ".";
@@ -336,7 +285,7 @@ void CommandEncoder::commit() {
    graph_key_ += ".";
    graph_key_ += std::to_string(empty_node_count_);

-    CudaGraphExec& graph_exec = graph_cache_[graph_key_];
+    cudaGraphExec_t& graph_exec = graph_cache_[graph_key_];

    if (graph_exec != nullptr) {
      cudaGraphExecUpdateResult update_result;
@@ -350,24 +299,31 @@ void CommandEncoder::commit() {
 #endif // CUDART_VERSION >= 12000
      if (update_result != cudaGraphExecUpdateSuccess) {
        cudaGetLastError(); // reset error
-        graph_exec.reset();
+        CHECK_CUDA_ERROR(cudaGraphExecDestroy(graph_exec));
+        graph_exec = nullptr;
      }
    }
    if (graph_exec == nullptr) {
-      graph_exec.instantiate(graph_);
+      CHECK_CUDA_ERROR(
+          cudaGraphInstantiate(&graph_exec, graph_, NULL, NULL, 0));
    }
    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();
-    graph_ = CudaGraph(device_);
+    CHECK_CUDA_ERROR(cudaGraphDestroy(graph_));
+    CHECK_CUDA_ERROR(cudaGraphCreate(&graph_, 0));
  }

  // Put completion handlers in a batch.
--- a/mlx/backend/cuda/device.h
+++ b/mlx/backend/cuda/device.h
@@ -3,7 +3,6 @@
 #pragma once

 #include "mlx/array.h"
-#include "mlx/backend/cuda/lru_cache.h"
 #include "mlx/backend/cuda/worker.h"
 #include "mlx/stream.h"

@@ -21,7 +20,7 @@ class CommandEncoder {
  struct CaptureContext {
    CaptureContext(CommandEncoder& enc);
    ~CaptureContext();
-    CudaGraph graph;
+    cudaGraph_t graph;
    CommandEncoder& enc;
    bool discard{false};
  };
@@ -32,6 +31,7 @@ class CommandEncoder {
  };

  explicit CommandEncoder(Device& d);
+  ~CommandEncoder();

  CommandEncoder(const CommandEncoder&) = delete;
  CommandEncoder& operator=(const CommandEncoder&) = delete;
@@ -47,35 +47,29 @@ 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,
-      uint32_t smem_bytes,
-      Params&&... params) {
+  void
+  add_kernel_node(F* func, dim3 grid_dim, dim3 block_dim, 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, smem_bytes, ptrs);
+    add_kernel_node((void*)func, grid_dim, block_dim, 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,
-      uint32_t smem_bytes,
-      void** params);
+  void
+  add_kernel_node(void* func, dim3 grid_dim, dim3 block_dim, 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) {
@@ -98,9 +92,6 @@ class CommandEncoder {
  void synchronize();

 private:
-  void add_kernel_node(const cudaKernelNodeParams& params);
-  void add_kernel_node(const CUDA_KERNEL_NODE_PARAMS& params);
-
  struct GraphNode {
    cudaGraphNode_t node;
    // K = kernel
@@ -115,7 +106,7 @@ class CommandEncoder {

  Device& device_;
  CudaStream stream_;
-  CudaGraph graph_;
+  cudaGraph_t graph_;
  Worker worker_;
  char node_count_{0};
  char graph_node_count_{0};
@@ -126,7 +117,7 @@ class CommandEncoder {
  std::string graph_key_;
  std::vector<GraphNode> concurrent_nodes_;
  std::vector<std::shared_ptr<array::Data>> temporaries_;
-  LRUCache<std::string, CudaGraphExec> graph_cache_;
+  std::unordered_map<std::string, cudaGraphExec_t> 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_;
--- a/mlx/backend/cuda/device/binary_ops.cuh
+++ b/mlx/backend/cuda/device/binary_ops.cuh
@@ -204,12 +204,6 @@ struct Power {
  __device__ T operator()(T base, T exp) {
    if constexpr (cuda::std::is_integral_v<T>) {
      T res = 1;
-      // Raising an integer to a negative power is undefined
-      if constexpr (cuda::std::is_signed_v<T>) {
-        if (exp < 0) {
-          return 0;
-        }
-      }
      while (exp) {
        if (exp & 1) {
          res *= base;
--- a/mlx/backend/cuda/device/cast_op.cuh
+++ b/mlx/backend/cuda/device/cast_op.cuh
@@ -6,6 +6,7 @@

 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
+#include <thrust/iterator/transform_iterator.h>

 namespace mlx::core::cu {

@@ -115,4 +116,15 @@ inline __host__ __device__ auto cast_to(SrcT x) {
  return CastOp<SrcT, DstT>{}(x);
 }

+// Return an iterator that cast the value to DstT using CastOp.
+template <typename DstT, typename Iterator>
+inline __host__ __device__ auto make_cast_iterator(Iterator it) {
+  using SrcT = typename cuda::std::iterator_traits<Iterator>::value_type;
+  if constexpr (std::is_same_v<SrcT, DstT>) {
+    return it;
+  } else {
+    return thrust::make_transform_iterator(it, CastOp<SrcT, DstT>{});
+  }
+}
+
 } // namespace mlx::core::cu
--- a/mlx/backend/cuda/device/utils.cuh
+++ b/mlx/backend/cuda/device/utils.cuh
@@ -43,18 +43,10 @@ struct alignas(sizeof(T) * N) AlignedVector {
 };

 template <int N, typename T>
-inline __host__ __device__ bool is_aligned(T* x) {
+inline __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,
@@ -109,13 +101,6 @@ inline __device__ AlignedVector<T, N> load_vector(
  }
 }

-template <int N, typename T>
-inline __device__ void
-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) {
@@ -146,22 +131,19 @@ inline __device__ void store_vector(
  }
 }

-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];
-    }
-  }
+// Helper for accessing strided data.
+template <typename T>
+struct StridedIterator {
+  T it;
+  int64_t stride;
+
+  __host__ __device__ StridedIterator(T it, int64_t stride)
+      : it(it), stride(stride) {}
+
+  __host__ __device__ auto operator[](int i) const {
+    return it[i * stride];
  }
+};

 ///////////////////////////////////////////////////////////////////////////////
 // Type limits utils
--- a/mlx/backend/cuda/distributed.cu
+++ b/mlx/backend/cuda/distributed.cu
@@ -1,56 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/distributed/primitives.h"
-#include "mlx/primitives.h"
-
-#include <cassert>
-
-namespace mlx::core::distributed {
-void AllReduce::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  assert(inputs.size() == 1);
-  assert(outputs.size() == 1);
-
-  auto set_input_output =
-      [s = stream()](const array& in, array& out) -> std::pair<array, array> {
-    if (!in.flags().row_contiguous) {
-      copy_gpu(in, out, CopyType::General, s);
-      return {out, out};
-    } else if (in.is_donatable()) {
-      out.copy_shared_buffer(in);
-      return {in, out};
-    } else {
-      out.set_data(allocator::malloc(out.nbytes()));
-      return {in, out};
-    }
-  };
-
-  auto [input, output] = set_input_output(inputs[0], outputs[0]);
-
-  auto& encoder = cu::get_command_encoder(stream());
-  encoder.set_input_array(input);
-  encoder.set_output_array(output);
-
-  auto capture = encoder.capture_context();
-  auto& s = stream();
-
-  switch (reduce_type_) {
-    case Sum:
-      distributed::detail::all_sum(group(), input, output, s);
-      break;
-    case Max:
-      distributed::detail::all_max(group(), input, output, s);
-      break;
-    case Min:
-      distributed::detail::all_min(group(), input, output, s);
-      break;
-    default:
-      throw std::runtime_error(
-          "Only all reduce sum, max, and min are supported.");
-  }
-}
-} // namespace mlx::core::distributed
--- a/mlx/backend/cuda/eval.cpp
+++ b/mlx/backend/cuda/eval.cpp
@@ -36,15 +36,18 @@ 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()) {
-    // Except for the donated one.
-    if (in.data_shared_ptr() != arr.data_shared_ptr()) {
-      encoder.add_temporary(in);
-    }
+    buffers.insert(in.data_shared_ptr());
  }
  for (auto& s : arr.siblings()) {
-    encoder.add_temporary(s);
+    buffers.insert(s.data_shared_ptr());
  }
+  // 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();
 }

--- a/mlx/backend/cuda/gemms/cublas_batched_gemm_12_0.cpp
+++ b/mlx/backend/cuda/gemms/cublas_batched_gemm_12_0.cpp
@@ -4,17 +4,16 @@
 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/gemms/cublas_gemm.h"

-namespace mlx::core {
+namespace mlx::core::cu {

-void CublasGemm::run_batched(
+void Matmul::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,
-    float alpha) {
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides) {
  encoder.set_input_array(a);
  encoder.set_input_array(b);
  encoder.set_output_array(out);
@@ -23,28 +22,27 @@ void CublasGemm::run_batched(
  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(
+    run_impl(
        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,
-        alpha);
+        nullptr);
    a_it.step();
    b_it.step();
  }
 }

-void CublasGemm::run_batched(
+void Matmul::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,
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides,
+    const mlx::core::Strides& c_batch_strides,
    float alpha,
    float beta) {
  encoder.set_input_array(a);
@@ -58,7 +56,7 @@ void CublasGemm::run_batched(
  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(
+    run_impl(
        encoder,
        out.data<int8_t>() + out.itemsize() * i * batch_shape.back() * M_ * N_,
        a.data<int8_t>() + a.itemsize() * a_it.loc,
@@ -72,4 +70,4 @@ void CublasGemm::run_batched(
  }
 }

-} // namespace mlx::core
+} // namespace mlx::core::cu
--- a/mlx/backend/cuda/gemms/cublas_batched_gemm_12_9.cu
+++ b/mlx/backend/cuda/gemms/cublas_batched_gemm_12_9.cu
@@ -0,0 +1,206 @@
+// 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::cu {
+
+namespace cg = cooperative_groups;
+
+__global__ void set_mm_device_pointers(
+    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;
+}
+
+__global__ void set_addmm_device_pointers(
+    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;
+}
+
+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)));
+}
+
+void Matmul::run_batched(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides) {
+  auto 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(uint64_t) * 3),
+      {static_cast<int>(batch_count * 3)},
+      uint64);
+
+  encoder.add_temporary(pointers);
+  int block_size = 512;
+  encoder.set_output_array(pointers);
+
+  encoder.add_kernel_node(
+      cu::set_mm_device_pointers,
+      cuda::ceil_div(pointers.size(), block_size),
+      block_size,
+      pointers.data<int8_t*>(),
+      a.data<int8_t>(),
+      b.data<int8_t>(),
+      out.data<int8_t>(),
+      static_cast<int>(out.dtype().size()),
+      const_param(batch_shape),
+      const_param(a_batch_strides),
+      const_param(b_batch_strides),
+      static_cast<int64_t>(M_) * N_,
+      static_cast<int>(batch_shape.size()),
+      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;
+  run_impl(
+      encoder,
+      reinterpret_cast<void*>(out_pointers),
+      reinterpret_cast<void*>(a_pointers),
+      reinterpret_cast<void*>(b_pointers),
+      nullptr);
+}
+
+void Matmul::run_batched(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const array& c,
+    const mlx::core::Shape& batch_shape,
+    const mlx::core::Strides& a_batch_strides,
+    const mlx::core::Strides& b_batch_strides,
+    const mlx::core::Strides& c_batch_strides,
+    float alpha,
+    float beta) {
+  auto 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),
+      {static_cast<int>(batch_count * 4)},
+      uint64);
+
+  encoder.add_temporary(pointers);
+  int block_size = 512;
+  encoder.set_output_array(pointers);
+  encoder.add_kernel_node(
+      cu::set_addmm_device_pointers,
+      cuda::ceil_div(pointers.size(), block_size),
+      block_size,
+      pointers.data<int8_t*>(),
+      a.data<int8_t>(),
+      b.data<int8_t>(),
+      c.data<int8_t>(),
+      out.data<int8_t>(),
+      static_cast<int>(out.dtype().size()),
+      const_param(batch_shape),
+      const_param(a_batch_strides),
+      const_param(b_batch_strides),
+      const_param(c_batch_strides),
+      static_cast<int64_t>(M_) * N_,
+      static_cast<int>(batch_shape.size()),
+      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;
+  run_impl(
+      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::cu
--- a/mlx/backend/cuda/gemms/cublas_gemm.cpp
+++ b/mlx/backend/cuda/gemms/cublas_gemm.cpp
@@ -7,12 +7,10 @@

 #include <fmt/format.h>

-namespace mlx::core {
-
-namespace {
+namespace mlx::core::cu {

 struct CublasPreference {
-  CublasPreference(cu::Device& device) {
+  CublasPreference(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
@@ -35,7 +33,7 @@ struct CublasPreference {
  cublasLtMatmulPreference_t pref_{nullptr};
 };

-cublasLtMatmulPreference_t cublas_preference(cu::Device& device) {
+cublasLtMatmulPreference_t cublas_preference(Device& device) {
  static CublasPreference pref(device);
  return pref.pref_;
 }
@@ -54,7 +52,7 @@ cublasComputeType_t dtype_to_compute_type(Dtype dtype) {
      return CUBLAS_COMPUTE_64F;
    default:
      throw std::runtime_error(fmt::format(
-          "Unsupported dtype in CublasGemm: {}.", dtype_to_string(dtype)));
+          "Unsupported dtype in Matmul: {}.", dtype_to_string(dtype)));
  }
 }

@@ -72,7 +70,7 @@ cudaDataType_t dtype_to_cublas_type(Dtype dtype) {
      return CUDA_C_32F;
    default:
      throw std::runtime_error(fmt::format(
-          "Unsupported dtype in CublasGemm: {}.", dtype_to_string(dtype)));
+          "Unsupported dtype in Matmul: {}.", dtype_to_string(dtype)));
  }
 }

@@ -85,10 +83,10 @@ cublasLtMatrixLayout_t create_matrix_layout(
    int32_t batch_count,
    int64_t batch_stride) {
  cublasLtMatrixLayout_t desc;
-  if (transposed) {
-    std::swap(rows, cols);
-  }
  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,
@@ -104,10 +102,8 @@ cublasLtMatrixLayout_t create_matrix_layout(
  return desc;
 }

-} // namespace
-
-CublasGemm::CublasGemm(
-    cu::Device& device,
+Matmul::Matmul(
+    Device& device,
    Dtype dtype,
    bool a_transposed,
    uint64_t a_rows,
@@ -138,38 +134,29 @@ CublasGemm::CublasGemm(
      CUBLASLT_MATMUL_DESC_POINTER_MODE,
      &pointer_mode,
      sizeof(int32_t)));
-
-  // In cublasLt matrices use column-major layout, while it is possible to use
-  // the CUBLASLT_ORDER_ROW option to switch to row-major layout, the bias
-  // epilogue does not work with the option. So instead we swap A and B to make
-  // cublasLt return the row-major result, which works because:
-  // - the data of a matrix in row-major layout is identical to its transpose in
-  //   column-major layout
-  // - C^T = (A @ B)^T = B^T @ A^T
-  cublasOperation_t a_op = b_transposed ? CUBLAS_OP_T : CUBLAS_OP_N;
+  cublasOperation_t op = CUBLAS_OP_N;
  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
      matmul_desc_,
      CUBLASLT_MATMUL_DESC_TRANSA,
-      &a_op,
+      &op,
      sizeof(cublasOperation_t)));
-  cublasOperation_t b_op = a_transposed ? CUBLAS_OP_T : CUBLAS_OP_N;
  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
      matmul_desc_,
      CUBLASLT_MATMUL_DESC_TRANSB,
-      &b_op,
+      &op,
      sizeof(cublasOperation_t)));

  auto type = dtype_to_cublas_type(dtype);
  a_desc_ = create_matrix_layout(
-      type, b_cols, b_rows, b_transposed, ldb, batch_count, b_batch_stride);
+      type, a_rows, a_cols, a_transposed, lda, batch_count, a_batch_stride);
  b_desc_ = create_matrix_layout(
-      type, a_cols, a_rows, a_transposed, lda, batch_count, a_batch_stride);
+      type, b_rows, b_cols, b_transposed, ldb, batch_count, b_batch_stride);
  out_desc_ = create_matrix_layout(
-      type, b_cols, a_rows, false, b_cols, batch_count, a_rows * b_cols);
+      type, a_rows, b_cols, false, b_cols, batch_count, a_rows * b_cols);
 }

-CublasGemm::CublasGemm(
-    cu::Device& device,
+Matmul::Matmul(
+    Device& device,
    Dtype dtype,
    bool a_transposed,
    uint64_t a_rows,
@@ -184,7 +171,7 @@ CublasGemm::CublasGemm(
    int64_t a_batch_stride,
    int64_t b_batch_stride,
    int64_t c_batch_stride)
-    : CublasGemm(
+    : Matmul(
          device,
          dtype,
          a_transposed,
@@ -200,10 +187,10 @@ CublasGemm::CublasGemm(
          b_batch_stride) {
  auto type = dtype_to_cublas_type(dtype);
  c_desc_ = create_matrix_layout(
-      type, b_cols, a_rows, false, ldc, batch_count, c_batch_stride);
+      type, a_rows, b_cols, false, ldc, batch_count, c_batch_stride);
 }

-CublasGemm::~CublasGemm() {
+Matmul::~Matmul() {
  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(a_desc_));
  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(b_desc_));
  CHECK_CUBLAS_ERROR(cublasLtMatrixLayoutDestroy(c_desc_));
@@ -211,122 +198,7 @@ CublasGemm::~CublasGemm() {
  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),
-      cols,
-      rows,
-      transposed,
-      ld,
-      batch_count,
-      batch_stride);
-}
-
-void CublasGemm::set_bias(cu::CommandEncoder& encoder, const array& bias) {
-  encoder.set_input_array(bias);
-  cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_BIAS;
-  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-      matmul_desc_,
-      CUBLASLT_MATMUL_DESC_EPILOGUE,
-      &epilogue,
-      sizeof(epilogue)));
-  auto* bias_ptr = bias.data<void>();
-  CHECK_CUBLAS_ERROR(cublasLtMatmulDescSetAttribute(
-      matmul_desc_,
-      CUBLASLT_MATMUL_DESC_BIAS_POINTER,
-      &bias_ptr,
-      sizeof(bias_ptr)));
-}
-
-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,
-    float alpha) {
-  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,
-        alpha);
-    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,
-      alpha);
-}
-
-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(
+void Matmul::run_impl(
    cu::CommandEncoder& encoder,
    void* out,
    const void* a,
@@ -341,7 +213,7 @@ void CublasGemm::execute(
        matmul_desc_,
        a_desc_,
        b_desc_,
-        c ? c_desc_ : out_desc_,
+        out_desc_, // TODO should that be c_desc is it's set?
        out_desc_,
        pref_,
        1,
@@ -354,10 +226,8 @@ void CublasGemm::execute(

  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),
+        allocator::malloc(heuristic_.workspaceSize),
        {static_cast<int>(heuristic_.workspaceSize)},
        int8);
    encoder.add_temporary(workspace);
@@ -369,9 +239,9 @@ void CublasGemm::execute(
      handle_,
      matmul_desc_,
      &alpha,
-      b, // a and b are swapped
-      a_desc_,
      a,
+      a_desc_,
+      b,
      b_desc_,
      &beta,
      c ? c : out,
@@ -384,4 +254,29 @@ void CublasGemm::execute(
      encoder.stream()));
 }

-} // namespace mlx::core
+void Matmul::run(
+    cu::CommandEncoder& encoder,
+    array& out,
+    const array& a,
+    const array& b,
+    const std::optional<array>& c /* = std::nullopt */,
+    float alpha /* = 1 */,
+    float beta /* = 0 */) {
+  encoder.set_input_array(a);
+  encoder.set_input_array(b);
+  if (c) {
+    encoder.set_input_array(*c);
+  }
+  encoder.set_output_array(out);
+
+  run_impl(
+      encoder,
+      out.data<void>(),
+      a.data<void>(),
+      b.data<void>(),
+      c ? c->data<void>() : nullptr,
+      alpha,
+      beta);
+}
+
+} // namespace mlx::core::cu
--- a/mlx/backend/cuda/gemms/cublas_gemm.h
+++ b/mlx/backend/cuda/gemms/cublas_gemm.h
@@ -5,13 +5,13 @@
 #include "mlx/backend/cuda/device.h"

 #include <cublasLt.h>
+#include <optional>

-namespace mlx::core {
-
-class CublasGemm {
+namespace mlx::core::cu {
+class Matmul {
 public:
-  CublasGemm(
-      cu::Device& device,
+  Matmul(
+      Device& device,
      Dtype dtype,
      bool a_transposed,
      uint64_t a_rows,
@@ -25,8 +25,8 @@ class CublasGemm {
      int64_t a_batch_stride,
      int64_t b_batch_stride);

-  CublasGemm(
-      cu::Device& device,
+  Matmul(
+      Device& device,
      Dtype dtype,
      bool a_transposed,
      uint64_t a_rows,
@@ -42,69 +42,41 @@ class CublasGemm {
      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 set_bias(cu::CommandEncoder& encoder, const array& bias);
+  ~Matmul();

  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,
-      float alpha = 1.0f);
+      const std::optional<array>& c = std::nullopt,
+      float alpha = 1,
+      float beta = 0);

-  void run(
+  void run_batched(
+      cu::CommandEncoder& encoder,
+      array& out,
+      const array& a,
+      const array& b,
+      const mlx::core::Shape& batch_shape,
+      const mlx::core::Strides& a_batch_strides,
+      const mlx::core::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,
+      const mlx::core::Shape& batch_shape,
+      const mlx::core::Strides& a_batch_strides,
+      const mlx::core::Strides& b_batch_strides,
+      const mlx::core::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,
-      float alpha);
-
-  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(
+  void run_impl(
      cu::CommandEncoder& encoder,
      void* out,
      const void* a,
@@ -125,4 +97,4 @@ class CublasGemm {
  cublasLtMatmulHeuristicResult_t heuristic_;
 };

-} // namespace mlx::core
+} // namespace mlx::core::cu
--- a/mlx/backend/cuda/gemms/cublas_gemm_batched_12_9.cu
+++ b/mlx/backend/cuda/gemms/cublas_gemm_batched_12_9.cu
@@ -1,329 +0,0 @@
-// 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,
-    float alpha) {
-  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,
-      alpha);
-}
-
-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
--- a/mlx/backend/cuda/gemms/gemv.cu
+++ b/mlx/backend/cuda/gemms/gemv.cu
@@ -27,9 +27,8 @@ gemv_impl(const T* mat, const T* vec, T* out, int rows, int cols) {
    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);
+      auto local_mat = load_vector<n_per_thread>(mat + row * cols + col, 0);
+      auto local_vec = load_vector<n_per_thread>(vec + col, 0);
 #pragma unroll
      for (int j = 0; j < n_per_thread; ++j) {
        sum +=
@@ -128,13 +127,9 @@ void gemv(
      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;
+    int n_per_t = 4;
+    while (K % (n_per_t * WARP_SIZE) != 0) {
+      n_per_t >>= 1;
    }
    dispatch_n_per_thread(n_per_t, [&](auto n_per_thread) {
      if (batch_count == 1) {
@@ -143,7 +138,6 @@ void gemv(
            kernel,
            num_blocks_x,
            block_dims,
-            0,
            mat,
            vec,
            out.data<DataType>(),
@@ -155,7 +149,6 @@ void gemv(
            kernel,
            dim3{num_blocks_x, batch_count},
            block_dims,
-            0,
            mat,
            vec,
            out.data<DataType>(),
--- a/mlx/backend/cuda/indexing.cpp
+++ b/mlx/backend/cuda/indexing.cpp
@@ -29,12 +29,12 @@ void append_indices_arg(
    const std::vector<array>& inputs,
    int nidx,
    int idx_ndim) {
-  SmallVector<const void*> indices(nidx);
+  std::vector<const void*> indices(nidx);
  for (int i = 0; i < nidx; ++i) {
    indices[i] = inputs[i + 1].data<void>();
  }
  args.append(std::move(indices));
-  SmallVector<int32_t> indices_shape(nidx * idx_ndim);
+  std::vector<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));
-  SmallVector<int64_t> indices_strides(nidx * idx_ndim);
+  std::vector<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_tuple(false, jit_source_gather, std::move(kernel_names));
+    return std::make_pair(jit_source_gather, std::move(kernel_names));
  });

  cu::KernelArgs args;
@@ -129,7 +129,7 @@ void Gather::eval_gpu(const std::vector<array>& inputs, array& out) {

  auto kernel = mod.get_kernel(kernel_name);
  auto [num_blocks, block_dims] = get_launch_args(out, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, 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_tuple(false, jit_source_scatter, std::move(kernel_names));
+    return std::make_pair(jit_source_scatter, std::move(kernel_names));
  });

  cu::KernelArgs args;
@@ -230,7 +230,7 @@ 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(upd, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }

 void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -268,8 +268,7 @@ void GatherAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
        }
      }
    }
-    return std::make_tuple(
-        false, jit_source_gather_axis, std::move(kernel_names));
+    return std::make_pair(jit_source_gather_axis, std::move(kernel_names));
  });

  size_t idx_size_pre = 1;
@@ -319,7 +318,7 @@ 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(idx, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }

 void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -372,8 +371,7 @@ void ScatterAxis::eval_gpu(const std::vector<array>& inputs, array& out) {
        }
      }
    }
-    return std::make_tuple(
-        false, jit_source_scatter_axis, std::move(kernel_names));
+    return std::make_pair(jit_source_scatter_axis, std::move(kernel_names));
  });

  size_t idx_size_pre = 1;
@@ -424,7 +422,7 @@ 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(idx, large);
-  encoder.add_kernel_node(kernel, num_blocks, block_dims, 0, args.args());
+  encoder.add_kernel_node(kernel, num_blocks, block_dims, args.args());
 }

 } // namespace mlx::core
--- a/mlx/backend/cuda/jit_module.cpp
+++ b/mlx/backend/cuda/jit_module.cpp
@@ -67,12 +67,10 @@ const std::string& cccl_dir() {
      return path.string();
    }
    // Finally check the environment variable.
-    if (const char* env = std::getenv("MLX_CCCL_DIR"); env) {
-      path = env;
+    path = std::getenv("MLX_CCCL_DIR");
    if (!path.empty() && std::filesystem::exists(path)) {
      return path.string();
    }
-    }
    return std::string();
  }();
  return dir;
@@ -103,8 +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::string& ptx,
-    std::vector<std::pair<std::string, std::string>>& ptx_kernels) {
+    std::vector<char>* ptx,
+    std::vector<std::pair<std::string, std::string>>* ptx_kernels) {
  if (cache_dir.empty()) {
    return false;
  }
@@ -119,15 +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;
@@ -137,7 +135,7 @@ bool read_cached_ptx(
 void write_cached_ptx(
    const std::filesystem::path& cache_dir,
    const std::string& module_name,
-    const std::string& ptx,
+    const std::vector<char>& ptx,
    const std::vector<std::pair<std::string, std::string>>& ptx_kernels,
    const std::string& source_code) {
  if (cache_dir.empty()) {
@@ -219,18 +217,22 @@ constexpr const char* g_headers[] = {
    jit_source_utils,
 };

-void compile(
+} // namespace
+
+JitModule::JitModule(
    Device& device,
    const std::string& module_name,
-    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
+    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.c_str(),
+        source_code.c_str(),
        (module_name + ".cu").c_str(),
        std::size(g_headers),
        g_headers,
@@ -284,20 +286,16 @@ void compile(
    } else {
      CHECK_NVRTC_ERROR(nvrtcGetPTXSize(prog, &ptx_size));
    }
-  ptx.resize(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);
  }

-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[] = {
@@ -314,69 +312,21 @@ void load_module(
  for (const auto& [name, mangled] : ptx_kernels) {
    CUfunction kernel;
    CHECK_CUDA_ERROR(cuModuleGetFunction(&kernel, module_, mangled.c_str()));
-    kernels[name] = std::make_pair(kernel, false);
+    kernels_[name] = kernel;
  }
 }

-} // 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,
-    std::function<void(CUfunction)> configure_kernel) {
+CUfunction JitModule::get_kernel(const std::string& kernel_name) {
  auto it = kernels_.find(kernel_name);
  if (it == kernels_.end()) {
    throw std::runtime_error(
        fmt::format("There is no kernel named {}.", kernel_name));
  }
-
-  // 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;
+  return it->second;
 }

 std::unordered_map<std::string, JitModule>& get_jit_module_cache() {
@@ -387,12 +337,11 @@ 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,
-    bool cache) {
+    const KernelBuilder& builder) {
  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, cache).first;
+    it = map.try_emplace(name, cu::device(device), name, builder).first;
  }
  return it->second;
 }
--- a/mlx/backend/cuda/jit_module.h
+++ b/mlx/backend/cuda/jit_module.h
@@ -19,8 +19,7 @@ namespace mlx::core::cu {

 class Device;

-using KernelBuilderResult = std::tuple<
-    /* precompiled */ bool,
+using KernelBuilderResult = std::pair<
    /* source code */ std::string,
    /* kernel names */ std::vector<std::string>>;
 using KernelBuilder = std::function<KernelBuilderResult()>;
@@ -41,19 +40,19 @@ struct KernelArgs {
  }

  template <typename T>
-  void append(SmallVector<T> vec) {
+  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));
-    append_ptr(std::get<SmallVector<T>>(storage_.back()).data());
    }
-
-  template <typename T>
-  void append(const std::vector<T>& vec) {
-    append(SmallVector<T>(vec.begin(), vec.end()));
  }

  // Make sure the arg is copied to an array with size of NDIM.
  template <size_t NDIM = MAX_NDIM, typename T>
-  void append_ndim(SmallVector<T> vec) {
+  void append_ndim(std::vector<T> vec) {
    if (vec.size() > NDIM) {
      throw std::runtime_error(
          fmt::format("ndim can not be larger than {}.", NDIM));
@@ -69,19 +68,17 @@ struct KernelArgs {
 private:
  std::vector<void*> args_;

-  // The cuGraphAddKernelNode API requires passing pointers to arguments so
-  // store temporary values until the node is created.
+  // The cuLaunchKernel API requires passing pointers to arguments so store
+  // temporary values untill kernel is launched.
  using Arg = std::variant<
      std::monostate,
      CUdeviceptr,
-      bool,
      int32_t,
      uint32_t,
      int64_t,
-      float,
-      SmallVector<const void*>,
-      SmallVector<int32_t>,
-      SmallVector<int64_t>>;
+      std::vector<const void*>,
+      std::vector<int32_t>,
+      std::vector<int64_t>>;
  std::deque<Arg> storage_;
 };

@@ -90,19 +87,16 @@ class JitModule {
  JitModule(
      Device& device,
      const std::string& module_name,
-      const KernelBuilder& builder,
-      bool cache);
+      const KernelBuilder& builder);
  ~JitModule();

  JitModule(const JitModule&) = delete;
  JitModule& operator=(const JitModule&) = delete;
-  CUfunction get_kernel(
-      const std::string& kernel_name,
-      std::function<void(CUfunction)> configure_kernel = nullptr);
+  CUfunction get_kernel(const std::string& kernel_name);

 private:
  CUmodule module_{nullptr};
-  std::unordered_map<std::string, std::pair<CUfunction, bool>> kernels_;
+  std::unordered_map<std::string, CUfunction> kernels_;
 };

 std::unordered_map<std::string, JitModule>& get_jit_module_cache();
@@ -110,7 +104,6 @@ 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,
-    bool use_disk_cache = true);
+    const KernelBuilder& builder);

 } // namespace mlx::core::cu
--- a/mlx/backend/cuda/kernel_utils.cuh
+++ b/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 SmallVector<T>& vec) {
+inline cuda::std::array<T, NDIM> const_param(const std::vector<T>& vec) {
  if (vec.size() > NDIM) {
    throw std::runtime_error(
        fmt::format("ndim can not be larger than {}.", NDIM));
--- a/mlx/backend/cuda/layer_norm.cu
+++ b/mlx/backend/cuda/layer_norm.cu
@@ -279,7 +279,6 @@ void LayerNorm::eval_gpu(
          kernel,
          n_rows,
          block_dim(),
-          0,
          x.data<DataType>(),
          w.data<DataType>(),
          b.data<DataType>(),
@@ -392,7 +391,6 @@ void LayerNormVJP::eval_gpu(
                kernel,
                n_rows,
                block_dim(),
-                0,
                x.data<DataType>(),
                w.data<DataType>(),
                g.data<DataType>(),
--- a/mlx/backend/cuda/logsumexp.cu
+++ b/mlx/backend/cuda/logsumexp.cu
@@ -150,7 +150,6 @@ void LogSumExp::eval_gpu(const std::vector<array>& inputs, array& out) {
          kernel,
          n_rows,
          block_dim(),
-          0,
          in.data<DataType>(),
          out.data<DataType>(),
          axis_size);
--- a/mlx/backend/cuda/lru_cache.h
+++ b/mlx/backend/cuda/lru_cache.h
@@ -2,7 +2,6 @@

 #pragma once

-#include <cstring>
 #include <list>
 #include <unordered_map>
 #include <utility>
@@ -21,11 +20,7 @@ class LRUCache {
  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.");
-    }
-  }
+  explicit LRUCache(size_t capacity) : capacity_(capacity) {}

  size_t size() const {
    return map_.size();
@@ -89,14 +84,6 @@ class LRUCache {
    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_) {
--- a/mlx/backend/cuda/matmul.cpp
+++ b/mlx/backend/cuda/matmul.cpp
@@ -11,7 +11,6 @@
 #include <numeric>

 namespace mlx::core {
-
 namespace {

 std::tuple<bool, int64_t, array>
@@ -29,76 +28,6 @@ check_transpose(cu::CommandEncoder& enc, const Stream& s, const array& arr) {
  }
 }

-void gemm_and_bias(
-    cu::CommandEncoder& encoder,
-    int M,
-    int N,
-    int K,
-    bool a_transposed,
-    int64_t lda,
-    bool b_transposed,
-    int64_t ldb,
-    array& out,
-    const array& a,
-    const array& b,
-    const std::optional<array>& bias = std::nullopt,
-    float alpha = 1.0f) {
-  // Check and collapse batch dimensions
-  auto [batch_shape, a_batch_strides, b_batch_strides] = collapse_batches(a, b);
-
-  auto batch_count = out.size() / (M * N);
-
-  // Collapse batches into M if needed
-  if (batch_count > 1 && !a_transposed && batch_shape.size() == 1 &&
-      a.strides()[a.ndim() - 2] == K && a_batch_strides.back() == M * K &&
-      b_batch_strides.back() == 0) {
-    M *= batch_shape.back();
-    batch_count = 1;
-
-    a_batch_strides = {0};
-    b_batch_strides = {0};
-    batch_shape = {1};
-  }
-
-  // Use gemmv when possible
-  if (!bias && cu::can_use_gemv(M, N, K, a_transposed, b_transposed)) {
-    cu::gemv(
-        a,
-        b,
-        out,
-        M,
-        N,
-        K,
-        batch_count,
-        batch_shape,
-        a_batch_strides,
-        b_batch_strides,
-        encoder);
-    return;
-  }
-
-  // Invoke cublasLt
-  CublasGemm gemm(
-      encoder.device(),
-      a.dtype(),
-      a_transposed,
-      M,
-      K,
-      lda,
-      b_transposed,
-      K,
-      N,
-      ldb,
-      batch_shape.back(),
-      a_batch_strides.back(),
-      b_batch_strides.back());
-  if (bias) {
-    gemm.set_bias(encoder, *bias);
-  }
-  gemm.run(
-      encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides, alpha);
-}
-
 } // namespace

 void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -119,6 +48,9 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {

  out.set_data(allocator::malloc(out.nbytes()));

+  /////////////////////////////////////////////////////////////////////////////
+  // Init checks and prep
+
  int M = a_pre.shape(-2);
  int N = b_pre.shape(-1);
  int K = a_pre.shape(-1);
@@ -128,8 +60,65 @@ void Matmul::eval_gpu(const std::vector<array>& inputs, array& out) {
  auto [a_transposed, lda, a] = check_transpose(encoder, s, a_pre);
  auto [b_transposed, ldb, b] = check_transpose(encoder, s, b_pre);

-  gemm_and_bias(
-      encoder, M, N, K, a_transposed, lda, b_transposed, ldb, out, a, b);
+  /////////////////////////////////////////////////////////////////////////////
+  // Check and collapse batch dimensions
+
+  auto [batch_shape, a_batch_strides, b_batch_strides] = collapse_batches(a, b);
+
+  auto batch_count = out.size() / (M * N);
+
+  // Collapse batches into M if needed
+  if (batch_count > 1 && !a_transposed && batch_shape.size() == 1 &&
+      a.strides()[a.ndim() - 2] == K && a_batch_strides.back() == M * K &&
+      b_batch_strides.back() == 0) {
+    M *= batch_shape.back();
+    batch_count = 1;
+
+    a_batch_strides = {0};
+    b_batch_strides = {0};
+    batch_shape = {1};
+  }
+
+  if (cu::can_use_gemv(M, N, K, a_transposed, b_transposed)) {
+    cu::gemv(
+        a,
+        b,
+        out,
+        M,
+        N,
+        K,
+        batch_count,
+        batch_shape,
+        a_batch_strides,
+        b_batch_strides,
+        encoder);
+    return;
+  }
+
+  /////////////////////////////////////////////////////////////////////////////
+  // Invoke cublasLt
+  cu::Matmul matmul(
+      cu::device(s.device),
+      a.dtype(),
+      a_transposed,
+      M,
+      K,
+      lda,
+      b_transposed,
+      K,
+      N,
+      ldb,
+      batch_shape.back(),
+      a_batch_strides.back(),
+      b_batch_strides.back());
+
+  if ((batch_count / batch_shape.back()) == 1) {
+    matmul.run(encoder, out, a, b);
+    return;
+  }
+
+  matmul.run_batched(
+      encoder, out, a, b, batch_shape, a_batch_strides, b_batch_strides);
 }

 void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
@@ -154,28 +143,6 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
  auto [a_transposed, lda, a] = check_transpose(encoder, s, a_pre);
  auto [b_transposed, ldb, b] = check_transpose(encoder, s, b_pre);

-  /////////////////////////////////////////////////////////////////////////////
-  // Dispatch to GEMM with epilogue or AddMM
-
-  if (beta_ == 1 && c.strides(-1) == 1 && c.data_size() == out.shape(-1)) {
-    out.set_data(allocator::malloc(out.nbytes()));
-    gemm_and_bias(
-        encoder,
-        M,
-        N,
-        K,
-        a_transposed,
-        lda,
-        b_transposed,
-        ldb,
-        out,
-        a,
-        b,
-        c,
-        alpha_);
-    return;
-  }
-
  int64_t ldc;
  {
    auto stx = c.strides()[c.ndim() - 2];
@@ -217,9 +184,9 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
  }

  /////////////////////////////////////////////////////////////////////////////
-  // Invoke cublasLt with AddMM settings
+  // Invoke cublasLt

-  CublasGemm gemm(
+  cu::Matmul matmul(
      cu::device(s.device),
      a.dtype(),
      a_transposed,
@@ -235,7 +202,12 @@ void AddMM::eval_gpu(const std::vector<array>& inputs, array& out) {
      a_batch_strides.back(),
      b_batch_strides.back(),
      c_batch_strides.back());
-  gemm.run(
+
+  if ((batch_count / batch_shape.back()) == 1) {
+    matmul.run(encoder, out, a, b, c, alpha_, beta_);
+    return;
+  }
+  matmul.run_batched(
      encoder,
      out,
      a,
--- a/mlx/backend/cuda/no_cuda.cpp
+++ b/mlx/backend/cuda/no_cuda.cpp
@@ -1,47 +1,11 @@
 // Copyright © 2025 Apple Inc.

 #include "mlx/backend/cuda/cuda.h"
-#include "mlx/fast.h"

-namespace mlx::core {
-
-namespace cu {
+namespace mlx::core::cu {

 bool is_available() {
  return false;
 }

-} // namespace cu
-
-namespace fast {
-
-CustomKernelFunction cuda_kernel(
-    const std::string&,
-    const std::vector<std::string>&,
-    const std::vector<std::string>&,
-    const std::string&,
-    const std::string&,
-    bool,
-    int) {
-  throw std::runtime_error("[cuda_kernel] No CUDA back-end.");
-}
-
-std::vector<array> precompiled_cuda_kernel(
-    const std::string&,
-    const std::string&,
-    const std::vector<array>&,
-    const std::vector<Shape>&,
-    const std::vector<Dtype>&,
-    const std::vector<ScalarArg>&,
-    std::tuple<int, int, int>,
-    std::tuple<int, int, int>,
-    int shared_memory,
-    std::optional<float> init_value,
-    bool ensure_row_contiguous,
-    StreamOrDevice) {
-  throw std::runtime_error("[cuda_kernel] No CUDA back-end.");
-}
-
-} // namespace fast
-
-} // namespace mlx::core
+} // namespace mlx::core::cu
--- a/mlx/backend/cuda/primitives.cpp
+++ b/mlx/backend/cuda/primitives.cpp
@@ -6,6 +6,17 @@

 namespace mlx::core {

+bool fast::ScaledDotProductAttention::use_fallback(
+    const array& q,
+    const array& k,
+    const array& v,
+    bool has_mask,
+    bool has_arr_mask,
+    bool do_causal,
+    Stream s) {
+  return true;
+}
+
 #define NO_GPU_MULTI(func)                                             \
  void func::eval_gpu(                                                 \
      const std::vector<array>& inputs, std::vector<array>& outputs) { \
@@ -24,6 +35,8 @@ namespace mlx::core {
  }

 NO_GPU(BlockMaskedMM)
+NO_GPU(DynamicSlice)
+NO_GPU(DynamicSliceUpdate)
 NO_GPU(FFT)
 NO_GPU(GatherMM)
 NO_GPU(GatherQMM)
@@ -39,7 +52,13 @@ NO_GPU(Cholesky)
 NO_GPU_MULTI(Eig)
 NO_GPU_MULTI(Eigh)

+namespace fast {
+NO_GPU(ScaledDotProductAttention)
+NO_GPU_MULTI(CustomKernel)
+} // namespace fast
+
 namespace distributed {
+NO_GPU_MULTI(AllReduce)
 NO_GPU_MULTI(AllGather)
 NO_GPU_MULTI(Send)
 NO_GPU_MULTI(Recv)
--- a/mlx/backend/cuda/quantized/affine_quantize.cu
+++ b/mlx/backend/cuda/quantized/affine_quantize.cu
@@ -2,17 +2,30 @@

 #include "mlx/backend/cuda/device.h"
 #include "mlx/backend/cuda/kernel_utils.cuh"
-#include "mlx/backend/cuda/quantized/quantized_utils.cuh"
+#include "mlx/backend/gpu/copy.h"
 #include "mlx/dtype_utils.h"
+#include "mlx/fast_primitives.h"

 #include <cooperative_groups.h>
 #include <cooperative_groups/reduce.h>
+#include <nvtx3/nvtx3.hpp>

 namespace mlx::core {
 namespace cu {

 namespace cg = cooperative_groups;

+template <int bits, int wsize = 8>
+inline constexpr __device__ short get_pack_factor() {
+  return (bits == 3 || bits == 5) ? 8 : (bits == 6 ? 4 : wsize / bits);
+}
+
+template <int bits, int wsize = 8>
+inline constexpr __device__ short get_bytes_per_pack() {
+  constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
+  return power_of_2_bits ? (wsize / 8) : (bits == 5 ? 5 : 3);
+}
+
 template <typename T, int group_size, int bits>
 __global__ void
 affine_quantize(const T* w, uint8_t* out, T* scales, T* biases, size_t size) {
@@ -227,102 +240,140 @@ __global__ void affine_dequantize(
 }

 } // namespace cu
+namespace {

-void affine_quantize(
-    const array& w,
-    array& wq,
-    array& scales,
-    array& biases,
-    int group_size_,
-    int bits_,
+inline array ensure_row_contiguous(
+    const array& x,
    cu::CommandEncoder& enc,
    const Stream& s) {
-  // Calculate the number of elements per thread
-  int per_thread = group_size_ / WARP_SIZE;
-  size_t size = w.size() / per_thread;
-
-  // Calculate the thread grid that we need to launch
-  bool large = size > UINT_MAX;
-  auto grid_shape = w.shape();
-  grid_shape.back() /= per_thread;
-
-  enc.set_input_array(w);
-  enc.set_output_array(wq);
-  enc.set_output_array(scales);
-  enc.set_output_array(biases);
-  dispatch_float_types(w.dtype(), "affine_quantize", [&](auto type_tag) {
-    dispatch_groups(group_size_, [&](auto group_size) {
-      dispatch_bits(bits_, [&](auto bits) {
-        using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-        auto kernel = cu::affine_quantize<T, group_size.value, bits.value>;
-        auto [num_blocks, block_dims] =
-            get_launch_args(size, grid_shape, w.strides(), large);
-        enc.add_kernel_node(
-            kernel,
-            num_blocks,
-            block_dims,
-            0,
-            w.data<T>(),
-            wq.data<uint8_t>(),
-            scales.data<T>(),
-            biases.data<T>(),
-            w.size());
-      });
-    });
-  });
+  if (!x.flags().row_contiguous) {
+    array x_copy = contiguous_copy_gpu(x, s);
+    enc.add_temporary(x_copy);
+    return x_copy;
+  } else {
+    return x;
+  }
 }

-void affine_dequantize(
-    const array& wq,
-    const array& scales,
-    const array& biases,
-    array& w,
-    int group_size_,
-    int bits_,
-    cu::CommandEncoder& enc,
-    const Stream& s) {
-  // Calculate how many numbers we pack together. For 2, 4, 8 bits we pack in
-  // one uint8, for 3, 6 in 3 uint8 and for 5 in 5 uint8.
-  constexpr int uint8_per_uint32 = 4;
-  int packs_per_int;
-  switch (bits_) {
+} // namespace
+
+template <typename F>
+void dispatch_groups(int group_size, F&& f) {
+  switch (group_size) {
+    case 32:
+      f(std::integral_constant<int, 32>{});
+      break;
+    case 64:
+      f(std::integral_constant<int, 64>{});
+      break;
+    case 128:
+      f(std::integral_constant<int, 128>{});
+      break;
+  }
+}
+
+template <typename F>
+void dispatch_bits(int bits, F&& f) {
+  switch (bits) {
+    case 2:
+      f(std::integral_constant<int, 2>{});
+      break;
    case 3:
+      f(std::integral_constant<int, 3>{});
+      break;
+    case 4:
+      f(std::integral_constant<int, 4>{});
+      break;
    case 5:
-      packs_per_int = 8;
+      f(std::integral_constant<int, 5>{});
      break;
    case 6:
-      packs_per_int = 4;
+      f(std::integral_constant<int, 6>{});
      break;
-    default:
-      packs_per_int = 8 / bits_;
+    case 8:
+      f(std::integral_constant<int, 8>{});
+      break;
+  }
 }

-  size_t size = w.size() / packs_per_int;
-  bool large = size > UINT_MAX;
-  auto grid_shape = w.shape();
-  grid_shape.back() *= uint8_per_uint32;
+void fast::AffineQuantize::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  auto& w_pre = inputs[0];
+  auto& out = outputs[0];
+  out.set_data(allocator::malloc(out.nbytes()));

-  enc.set_input_array(wq);
+  auto& s = stream();
+  auto& d = cu::device(s.device);
+  auto& enc = d.get_command_encoder(s);
+
+  auto w = ensure_row_contiguous(w_pre, enc, s);
+  enc.set_input_array(w);
+  if (dequantize_) {
+    auto scales = ensure_row_contiguous(inputs[1], enc, s);
+    auto biases = ensure_row_contiguous(inputs[2], enc, s);
    enc.set_input_array(scales);
    enc.set_input_array(biases);
-  enc.set_output_array(w);
-  dispatch_float_types(w.dtype(), "affine_quantize", [&](auto type_tag) {
+    enc.set_output_array(out);
+  } else {
+    auto& scales = outputs[1];
+    auto& biases = outputs[2];
+    scales.set_data(allocator::malloc(scales.nbytes()));
+    biases.set_data(allocator::malloc(biases.nbytes()));
+    enc.set_output_array(out);
+    enc.set_output_array(scales);
+    enc.set_output_array(biases);
+  }
+
+  auto dtype = dequantize_ ? outputs[0].dtype() : inputs[0].dtype();
+
+  // Treat uint32 as uint8 in kernel
+  int uint8_per_uint32 = 4;
+  int packs_per_int = (bits_ == 3 || bits_ == 5) ? 8
+      : bits_ == 6                               ? 4
+                                                 : 8 / bits_;
+  int per_thread = dequantize_ ? packs_per_int : group_size_ / WARP_SIZE;
+  size_t size =
+      dequantize_ ? out.size() / packs_per_int : w.size() / per_thread;
+
+  bool large = size > UINT_MAX;
+  auto grid_shape = w.shape();
+
+  if (dequantize_) {
+    grid_shape.back() *= uint8_per_uint32;
+  } else {
+    grid_shape.back() /= per_thread;
+  }
+
+  dispatch_float_types(dtype, "affine_quantize", [&](auto type_tag) {
    dispatch_groups(group_size_, [&](auto group_size) {
      dispatch_bits(bits_, [&](auto bits) {
-        using T = cuda_type_t<MLX_GET_TYPE(type_tag)>;
-        auto kernel = cu::affine_dequantize<T, group_size.value, bits.value>;
+        using DataType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
+        if (dequantize_) {
          auto [num_blocks, block_dims] =
              get_launch_args(size, grid_shape, w.strides(), large);
          enc.add_kernel_node(
-            kernel,
+              cu::affine_dequantize<DataType, group_size.value, bits.value>,
              num_blocks,
              block_dims,
-            0,
-            wq.data<uint8_t>(),
-            scales.data<T>(),
-            biases.data<T>(),
-            w.data<T>(),
+              w.data<uint8_t>(),
+              inputs[1].data<DataType>(),
+              inputs[2].data<DataType>(),
+              out.data<DataType>(),
+              out.size());
+        } else {
+          auto [num_blocks, block_dims] =
+              get_launch_args(size, grid_shape, w.strides(), large);
+          enc.add_kernel_node(
+              cu::affine_quantize<DataType, group_size.value, bits.value>,
+              num_blocks,
+              block_dims,
+              w.data<DataType>(),
+              out.data<uint8_t>(),
+              outputs[1].data<DataType>(),
+              outputs[2].data<DataType>(),
              w.size());
+        }
      });
    });
  });
--- a/mlx/backend/cuda/quantized/quantized.cpp
+++ b/mlx/backend/cuda/quantized/quantized.cpp
@@ -1,80 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/quantized/quantized.h"
-#include "mlx/backend/cuda/device.h"
-#include "mlx/backend/gpu/copy.h"
-#include "mlx/fast_primitives.h"
-
-#include <nvtx3/nvtx3.hpp>
-
-namespace mlx::core {
-
-namespace {
-
-inline array ensure_row_contiguous(
-    const array& x,
-    cu::CommandEncoder& enc,
-    const Stream& s) {
-  if (!x.flags().row_contiguous) {
-    array x_copy = contiguous_copy_gpu(x, s);
-    enc.add_temporary(x_copy);
-    return x_copy;
-  } else {
-    return x;
-  }
-}
-
-inline array ensure_row_contiguous_matrix(
-    const array& x,
-    cu::CommandEncoder& enc,
-    const Stream& s) {
-  if (x.ndim() < 2) {
-    if (x.strides()[0] == 1) {
-      return x;
-    }
-  } else {
-    auto stride_0 = x.strides()[x.ndim() - 2];
-    auto stride_1 = x.strides()[x.ndim() - 1];
-    if (stride_0 == x.shape(-1) && stride_1 == 1) {
-      return x;
-    }
-  }
-  array x_copy = contiguous_copy_gpu(x, s);
-  enc.add_temporary(x_copy);
-  return x_copy;
-}
-
-} // namespace
-
-void fast::Quantize::eval_gpu(
-    const std::vector<array>& inputs,
-    std::vector<array>& outputs) {
-  nvtx3::scoped_range r("Quantize::eval_gpu");
-  auto& s = stream();
-  auto& d = cu::device(s.device);
-  auto& enc = d.get_command_encoder(s);
-
-  if (dequantize_) {
-    auto wq = ensure_row_contiguous(inputs[0], enc, s);
-    auto scales = ensure_row_contiguous(inputs[1], enc, s);
-    auto biases = ensure_row_contiguous(inputs[2], enc, s);
-    auto& w = outputs[0];
-
-    w.set_data(allocator::malloc(w.nbytes()));
-
-    affine_dequantize(wq, scales, biases, w, group_size_, bits_, enc, s);
-  } else {
-    auto w = ensure_row_contiguous(inputs[0], enc, s);
-    auto& wq = outputs[0];
-    auto& scales = outputs[1];
-    auto& biases = outputs[2];
-
-    wq.set_data(allocator::malloc(wq.nbytes()));
-    scales.set_data(allocator::malloc(scales.nbytes()));
-    biases.set_data(allocator::malloc(biases.nbytes()));
-
-    affine_quantize(w, wq, scales, biases, group_size_, bits_, enc, s);
-  }
-}
-
-} // namespace mlx::core
--- a/mlx/backend/cuda/quantized/quantized.h
+++ b/mlx/backend/cuda/quantized/quantized.h
@@ -1,27 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-#include "mlx/backend/cuda/device.h"
-
-namespace mlx::core {
-
-void affine_quantize(
-    const array& w,
-    array& wq,
-    array& scales,
-    array& biases,
-    int group_size_,
-    int bits_,
-    cu::CommandEncoder& enc,
-    const Stream& s);
-
-void affine_dequantize(
-    const array& wq,
-    const array& scales,
-    const array& biases,
-    array& w,
-    int group_size_,
-    int bits_,
-    cu::CommandEncoder& enc,
-    const Stream& s);
-
-} // namespace mlx::core
--- a/mlx/backend/cuda/quantized/quantized_utils.cuh
+++ b/mlx/backend/cuda/quantized/quantized_utils.cuh
@@ -1,59 +0,0 @@
-// Copyright © 2025 Apple Inc.
-
-namespace mlx::core {
-
-namespace cu {
-
-template <int bits, int wsize = 8>
-inline constexpr __device__ short get_pack_factor() {
-  return (bits == 3 || bits == 5) ? 8 : (bits == 6 ? 4 : wsize / bits);
-}
-
-template <int bits, int wsize = 8>
-inline constexpr __device__ short get_bytes_per_pack() {
-  constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
-  return power_of_2_bits ? (wsize / 8) : (bits == 5 ? 5 : 3);
-}
-
-} // namespace cu
-
-template <typename F>
-void dispatch_groups(int group_size, F&& f) {
-  switch (group_size) {
-    case 32:
-      f(std::integral_constant<int, 32>{});
-      break;
-    case 64:
-      f(std::integral_constant<int, 64>{});
-      break;
-    case 128:
-      f(std::integral_constant<int, 128>{});
-      break;
-  }
-}
-
-template <typename F>
-void dispatch_bits(int bits, F&& f) {
-  switch (bits) {
-    case 2:
-      f(std::integral_constant<int, 2>{});
-      break;
-    case 3:
-      f(std::integral_constant<int, 3>{});
-      break;
-    case 4:
-      f(std::integral_constant<int, 4>{});
-      break;
-    case 5:
-      f(std::integral_constant<int, 5>{});
-      break;
-    case 6:
-      f(std::integral_constant<int, 6>{});
-      break;
-    case 8:
-      f(std::integral_constant<int, 8>{});
-      break;
-  }
-}
-
-} // namespace mlx::core
--- a/mlx/backend/cuda/random.cu
+++ b/mlx/backend/cuda/random.cu
@@ -170,7 +170,6 @@ void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
        cu::rbitsc,
        grid,
        block,
-        0,
        keys.data<uint32_t>(),
        out.data<uint8_t>(),
        grid_dims,
@@ -181,7 +180,6 @@ void RandomBits::eval_gpu(const std::vector<array>& inputs, array& out) {
        cu::rbits,
        grid,
        block,
-        0,
        keys.data<uint32_t>(),
        out.data<uint8_t>(),
        grid_dims,
--- a/mlx/backend/cuda/reduce/all_reduce.cu
+++ b/mlx/backend/cuda/reduce/all_reduce.cu
@@ -120,7 +120,6 @@ void all_reduce(
            kernel,
            blocks,
            threads,
-            0,
            static_cast<T*>(indata),
            intermediate.data<U>(),
            block_step,
@@ -147,7 +146,6 @@ void all_reduce(
          kernel,
          blocks,
          threads,
-          0,
          static_cast<T*>(indata),
          out.data<U>(),
          block_step,
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Cheng	a5f2ce1275	Move pip install to python deps	2025-07-30 17:39:09 +09:00
Cheng	ad69a87050	Verbose install	2025-07-30 16:57:43 +09:00
Cheng	262882b6ca	Remove unnecessary uv	2025-07-30 15:30:54 +09:00
Cheng	4ba778c08a	Install uv	2025-07-30 15:27:00 +09:00
Cheng	893c55459e	There is no uvx	2025-07-30 15:20:40 +09:00
Cheng	8fb222f4a7	Revert "Save uv cache" This reverts commit `aaa7b822cc`.	2025-07-30 15:17:00 +09:00
Cheng	0bb6108fdf	uv is installed by default on linux	2025-07-30 15:15:32 +09:00
Cheng	acc09ed3a7	Use uv in mac	2025-07-30 15:02:49 +09:00
Cheng	aaa7b822cc	Save uv cache	2025-07-30 14:42:52 +09:00
Cheng	48e0eba372	Use uv in cuda build	2025-07-30 14:20:21 +09:00
Cheng	8d9bc0c421	Use uv to install packages in linux_build_and_test	2025-07-30 13:19:14 +09:00
Cheng	51da73c855	Remove unused apt installs	2025-07-30 13:11:38 +09:00