diff --git a/mlx/backend/cuda/CMakeLists.txt b/mlx/backend/cuda/CMakeLists.txt
index 854c6c116..3e7b859a6 100644
--- a/mlx/backend/cuda/CMakeLists.txt
+++ b/mlx/backend/cuda/CMakeLists.txt
@@ -30,6 +30,7 @@ target_sources(
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/col_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/row_reduce.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/reduce/segmented_reduce.cu
+          ${CMAKE_CURRENT_SOURCE_DIR}/rms_norm.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/slicing.cpp
           ${CMAKE_CURRENT_SOURCE_DIR}/softmax.cu
           ${CMAKE_CURRENT_SOURCE_DIR}/sort.cu
diff --git a/mlx/backend/cuda/layer_norm.cu b/mlx/backend/cuda/layer_norm.cu
index 5aa287603..c71795fad 100644
--- a/mlx/backend/cuda/layer_norm.cu
+++ b/mlx/backend/cuda/layer_norm.cu
@@ -244,8 +244,7 @@ void LayerNorm::eval_gpu(
     }
   };
 
-  array o = set_output(inputs[0]);
-  const array& x = o.data_shared_ptr() ? o : out;
+  const array x = set_output(inputs[0]);
   const array& w = inputs[1];
   const array& b = inputs[2];
 
diff --git a/mlx/backend/cuda/primitives.cu b/mlx/backend/cuda/primitives.cu
index 5fef9e150..95ea44f94 100644
--- a/mlx/backend/cuda/primitives.cu
+++ b/mlx/backend/cuda/primitives.cu
@@ -99,8 +99,6 @@ NO_GPU_MULTI(Eig)
 NO_GPU_MULTI(Eigh)
 
 namespace fast {
-NO_GPU_USE_FALLBACK(RMSNorm)
-NO_GPU_MULTI(RMSNormVJP)
 NO_GPU_USE_FALLBACK(RoPE)
 NO_GPU(ScaledDotProductAttention)
 NO_GPU_MULTI(AffineQuantize)
diff --git a/mlx/backend/cuda/rms_norm.cu b/mlx/backend/cuda/rms_norm.cu
new file mode 100644
index 000000000..3c521b90d
--- /dev/null
+++ b/mlx/backend/cuda/rms_norm.cu
@@ -0,0 +1,343 @@
+// Copyright © 2025 Apple Inc.
+
+#include "mlx/backend/cuda/device.h"
+#include "mlx/backend/cuda/iterators/strided_iterator.cuh"
+#include "mlx/backend/cuda/kernel_utils.cuh"
+#include "mlx/backend/cuda/reduce/reduce.cuh"
+#include "mlx/backend/gpu/copy.h"
+#include "mlx/dtype_utils.h"
+#include "mlx/fast_primitives.h"
+
+#include <cooperative_groups.h>
+#include <cooperative_groups/reduce.h>
+#include <nvtx3/nvtx3.hpp>
+#include <cub/block/block_load.cuh>
+#include <cub/block/block_reduce.cuh>
+
+namespace mlx::core {
+
+namespace cu {
+
+namespace cg = cooperative_groups;
+
+inline __device__ float2 plus_f2(const float2& a, const float2& b) {
+  return {a.x + b.x, a.y + b.y};
+}
+
+// Similar to cub::BlockReduce, but result is broadcasted to every thread.
+template <typename T, int BLOCK_DIM>
+struct BlockBroadcastReduce {
+  static_assert(WARP_SIZE <= BLOCK_DIM && BLOCK_DIM <= WARP_SIZE * WARP_SIZE);
+  static_assert(BLOCK_DIM % WARP_SIZE == 0);
+  using TempStorage = T[BLOCK_DIM / WARP_SIZE];
+
+  cg::thread_block& block;
+  TempStorage& temp;
+
+  template <typename Op>
+  __device__ T Reduce(const T& input, const Op& op, const T& init_value) {
+    auto warp = cg::tiled_partition<WARP_SIZE>(block);
+    T x = cg::reduce(warp, input, op);
+    if (warp.thread_rank() == 0) {
+      temp[warp.meta_group_rank()] = x;
+    }
+    block.sync();
+    x = warp.thread_rank() < warp.meta_group_size() ? temp[warp.thread_rank()]
+                                                    : init_value;
+    return cg::reduce(warp, x, op);
+  }
+
+  __device__ T Sum(const T& input) {
+    return Reduce(input, cg::plus<T>{}, T{});
+  }
+};
+
+template <typename T, int BLOCK_DIM, int N_READS = 4>
+__global__ void rms_norm(
+    const T* x,
+    const T* w,
+    T* out,
+    float eps,
+    int32_t axis_size,
+    int64_t w_stride) {
+  auto grid = cg::this_grid();
+  auto block = cg::this_thread_block();
+
+  using BlockReduceT = BlockBroadcastReduce<float, BLOCK_DIM>;
+  __shared__ typename BlockReduceT::TempStorage temp;
+
+  x += grid.block_rank() * axis_size;
+  out += grid.block_rank() * axis_size;
+
+  // Normalizer.
+  float normalizer = 0;
+  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
+    auto index = r * BLOCK_DIM + block.thread_rank();
+    T xn[N_READS];
+    cub::LoadDirectBlocked(index, x, xn, axis_size, 0);
+    for (int i = 0; i < N_READS; ++i) {
+      float t = static_cast<float>(xn[i]);
+      normalizer += t * t;
+    }
+  }
+  normalizer = BlockReduceT{block, temp}.Sum(normalizer);
+  normalizer = rsqrt(normalizer / axis_size + eps);
+
+  // Outputs.
+  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
+    auto index = r * BLOCK_DIM + block.thread_rank();
+    T xn[N_READS];
+    T wn[N_READS];
+    cub::LoadDirectBlocked(index, x, xn, axis_size);
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
+    for (int i = 0; i < N_READS; ++i) {
+      float norm = static_cast<float>(xn[i]) * normalizer;
+      xn[i] = wn[i] * static_cast<T>(norm);
+    }
+    cub::StoreDirectBlocked(index, out, xn, axis_size);
+  }
+}
+
+template <typename T, bool HAS_W, int BLOCK_DIM, int N_READS = 4>
+__global__ void rms_norm_vjp(
+    const T* x,
+    const T* w,
+    const T* g,
+    T* gx,
+    T* gw,
+    float eps,
+    int32_t axis_size,
+    int64_t w_stride) {
+  auto grid = cg::this_grid();
+  auto block = cg::this_thread_block();
+
+  using BlockReduceF = BlockBroadcastReduce<float, BLOCK_DIM>;
+  using BlockReduceF2 = BlockBroadcastReduce<float2, BLOCK_DIM>;
+  __shared__ union {
+    typename BlockReduceF::TempStorage f;
+    typename BlockReduceF2::TempStorage f2;
+  } temp;
+
+  x += grid.block_rank() * axis_size;
+  g += grid.block_rank() * axis_size;
+  gx += grid.block_rank() * axis_size;
+  gw += grid.block_rank() * axis_size;
+
+  // Normalizer.
+  float2 factors = {};
+  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
+    T xn[N_READS];
+    T wn[N_READS] = {};
+    T gn[N_READS] = {};
+    auto index = r * BLOCK_DIM + block.thread_rank();
+    cub::LoadDirectBlocked(index, x, xn, axis_size, 0);
+    cub::LoadDirectBlocked(index, g, gn, axis_size);
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
+    for (int i = 0; i < N_READS; i++) {
+      float t = static_cast<float>(xn[i]);
+      float wi = wn[i];
+      float gi = gn[i];
+      float wg = wi * gi;
+      factors = plus_f2(factors, {wg * t, t * t});
+    }
+  }
+  factors = BlockReduceF2{block, temp.f2}.Reduce(factors, plus_f2, {});
+  float meangwx = factors.x / axis_size;
+  float normalizer = rsqrt(factors.y / axis_size + eps);
+  float normalizer3 = normalizer * normalizer * normalizer;
+
+  // Outputs.
+  for (int r = 0; r < cuda::ceil_div(axis_size, BLOCK_DIM * N_READS); ++r) {
+    auto index = r * BLOCK_DIM + block.thread_rank();
+    T xn[N_READS];
+    T wn[N_READS];
+    T gn[N_READS];
+    cub::LoadDirectBlocked(index, x, xn, axis_size);
+    cub::LoadDirectBlocked(index, g, gn, axis_size);
+    cub::LoadDirectBlocked(index, strided_iterator(w, w_stride), wn, axis_size);
+    for (int i = 0; i < N_READS; i++) {
+      float xi = xn[i];
+      float wi = wn[i];
+      float gi = gn[i];
+      xn[i] = static_cast<T>(normalizer * wi * gi - xi * meangwx * normalizer3);
+      if constexpr (HAS_W) {
+        wn[i] = static_cast<T>(gi * xi * normalizer);
+      }
+    }
+    cub::StoreDirectBlocked(index, gx, xn, axis_size);
+    if constexpr (HAS_W) {
+      cub::StoreDirectBlocked(index, gw, wn, axis_size);
+    }
+  }
+}
+
+} // namespace cu
+
+namespace fast {
+
+bool RMSNorm::use_fallback(Stream s) {
+  return s.device == Device::cpu;
+}
+
+// TODO: There are duplicate code with backend/metal/normalization.cpp
+void RMSNorm::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  nvtx3::scoped_range r("RMSNorm::eval_gpu");
+  auto& s = stream();
+  auto& out = outputs[0];
+
+  // Make sure that the last dimension is contiguous.
+  auto set_output = [&s, &out](const array& x) {
+    bool no_copy = x.flags().contiguous && x.strides()[x.ndim() - 1] == 1;
+    if (no_copy && x.ndim() > 1) {
+      auto s = x.strides()[x.ndim() - 2];
+      no_copy &= (s == 0 || s == x.shape().back());
+    }
+    if (no_copy) {
+      if (x.is_donatable()) {
+        out.copy_shared_buffer(x);
+      } else {
+        out.set_data(
+            allocator::malloc(x.data_size() * x.itemsize()),
+            x.data_size(),
+            x.strides(),
+            x.flags());
+      }
+      return x;
+    } else {
+      auto x_copy = array(x.shape(), x.dtype(), nullptr, {});
+      copy_gpu(x, x_copy, CopyType::General, s);
+      out.copy_shared_buffer(x_copy);
+      return x_copy;
+    }
+  };
+
+  const array x = set_output(inputs[0]);
+  const array& w = inputs[1];
+
+  int32_t axis_size = x.shape().back();
+  int32_t n_rows = x.data_size() / axis_size;
+  int64_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
+
+  auto& encoder = cu::get_command_encoder(s);
+  encoder.set_input_array(x);
+  encoder.set_input_array(w);
+  encoder.set_output_array(out);
+  encoder.launch_kernel([&](cudaStream_t stream) {
+    MLX_SWITCH_FLOAT_TYPES_CHECKED(out.dtype(), "rms_norm", CTYPE, {
+      using DataType = cuda_type_t<CTYPE>;
+      constexpr uint32_t N_READS = 4;
+      MLX_SWITCH_BLOCK_DIM(cuda::ceil_div(axis_size, N_READS), BLOCK_DIM, {
+        auto kernel = cu::rms_norm<DataType, BLOCK_DIM, N_READS>;
+        kernel<<<n_rows, BLOCK_DIM, 0, stream>>>(
+            x.data<DataType>(),
+            w.data<DataType>(),
+            out.data<DataType>(),
+            eps_,
+            axis_size,
+            w_stride);
+      });
+    });
+  });
+}
+
+void RMSNormVJP::eval_gpu(
+    const std::vector<array>& inputs,
+    std::vector<array>& outputs) {
+  nvtx3::scoped_range r("RMSNormVJP::eval_gpu");
+  auto& s = stream();
+  auto& encoder = cu::get_command_encoder(s);
+
+  // Ensure row contiguity. We could relax this step by checking that the array
+  // is contiguous (no broadcasts or holes) and that the input strides are the
+  // same as the cotangent strides but for now this is simpler.
+  auto check_input = [&s](const array& x) -> std::pair<array, bool> {
+    if (x.flags().row_contiguous) {
+      return {x, false};
+    }
+    array x_copy(x.shape(), x.dtype(), nullptr, {});
+    copy_gpu(x, x_copy, CopyType::General, s);
+    return {x_copy, true};
+  };
+  bool donate_x = inputs[0].is_donatable();
+  bool donate_g = inputs[2].is_donatable();
+  auto [x, copied] = check_input(inputs[0]);
+  donate_x |= copied;
+  const array& w = inputs[1];
+  auto [g, g_copied] = check_input(inputs[2]);
+  donate_g |= g_copied;
+  array& gx = outputs[0];
+  array& gw = outputs[1];
+
+  // Check whether we had a weight.
+  bool has_w = w.ndim() != 0;
+
+  // Allocate space for the outputs.
+  bool g_in_gx = false;
+  if (donate_x) {
+    gx.copy_shared_buffer(x);
+  } else if (donate_g) {
+    gx.copy_shared_buffer(g);
+    g_in_gx = true;
+  } else {
+    gx.set_data(allocator::malloc(gx.nbytes()));
+  }
+  if (g_copied && !g_in_gx) {
+    encoder.add_temporary(g);
+  }
+
+  int32_t axis_size = x.shape().back();
+  int32_t n_rows = x.data_size() / axis_size;
+  int64_t w_stride = (w.ndim() == 1) ? w.strides()[0] : 0;
+
+  // Allocate a temporary to store the gradients for w and allocate the output
+  // gradient accumulators.
+  array gw_temp =
+      (has_w) ? array({n_rows, x.shape().back()}, gw.dtype(), nullptr, {}) : w;
+  if (has_w) {
+    if (!g_in_gx && donate_g) {
+      gw_temp.copy_shared_buffer(g);
+    } else {
+      gw_temp.set_data(allocator::malloc(gw_temp.nbytes()));
+      encoder.add_temporary(gw_temp);
+    }
+  }
+  gw.set_data(allocator::malloc(gw.nbytes()));
+
+  encoder.set_input_array(x);
+  encoder.set_input_array(w);
+  encoder.set_input_array(g);
+  encoder.set_output_array(gx);
+  encoder.set_output_array(gw_temp);
+  encoder.launch_kernel([&, x = x, g = g](cudaStream_t stream) {
+    MLX_SWITCH_FLOAT_TYPES_CHECKED(gx.dtype(), "rms_norm_vjp", CTYPE, {
+      using DataType = cuda_type_t<CTYPE>;
+      constexpr int N_READS = 4;
+      MLX_SWITCH_BOOL(has_w, HAS_W, {
+        MLX_SWITCH_BLOCK_DIM(cuda::ceil_div(axis_size, N_READS), BLOCK_DIM, {
+          auto kernel = cu::rms_norm_vjp<DataType, HAS_W, BLOCK_DIM, N_READS>;
+          kernel<<<n_rows, BLOCK_DIM, 0, stream>>>(
+              x.data<DataType>(),
+              w.data<DataType>(),
+              g.data<DataType>(),
+              gx.data<DataType>(),
+              gw_temp.data<DataType>(),
+              eps_,
+              axis_size,
+              w_stride);
+        });
+      });
+    });
+  });
+
+  if (has_w) {
+    ReductionPlan plan(
+        ReductionOpType::ContiguousStridedReduce, {n_rows}, {axis_size});
+    col_reduce(encoder, gw_temp, gw, Reduce::ReduceType::Sum, {0}, plan);
+  }
+}
+
+} // namespace fast
+
+} // namespace mlx::core