diff --git a/kernels/optimized/cpu/binary_ops.h b/kernels/optimized/cpu/binary_ops.h
index 3c2dfea4e39..3b25e011439 100644
--- a/kernels/optimized/cpu/binary_ops.h
+++ b/kernels/optimized/cpu/binary_ops.h
@@ -43,6 +43,8 @@ enum class ElementwiseOptimizedPath {
   kBroadcast2dBy1dReverseArguments,
   kBroadcastNdByNd,
   kBroadcastNdByNdReverseArguments,
+  kBroadcastLastDim,
+  kBroadcastLastDimReverseArguments,
 };
 
 namespace internal {
@@ -117,6 +119,12 @@ inline ElementwiseOptimizedPath select_broadcast_optimized_path(
     } else {
       return ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments;
     }
+  } else if (broadcast_dim == -1) {
+    if (std::count_if(lhs_begin, lhs_end, [](Tensor::SizesType x) { return x == 1; }) == 1) {
+      return ElementwiseOptimizedPath::kBroadcastLastDimReverseArguments;
+    } else {
+      return ElementwiseOptimizedPath::kBroadcastLastDim;
+    }
   }
   return ElementwiseOptimizedPath::kNone;
 }
diff --git a/kernels/optimized/cpu/op_mul.cpp b/kernels/optimized/cpu/op_mul.cpp
index 6c994c0dc51..d8d2f505313 100644
--- a/kernels/optimized/cpu/op_mul.cpp
+++ b/kernels/optimized/cpu/op_mul.cpp
@@ -11,6 +11,7 @@
 #include <executorch/kernels/optimized/vec/vec.h>
 #include <executorch/kernels/portable/cpu/scalar_utils.h>
 #include <executorch/kernels/portable/cpu/util/broadcast_util.h>
+#include <executorch/runtime/core/exec_aten/util/tensor_util.h> // IWYU pragma: export
 #include <executorch/runtime/kernel/kernel_includes.h>
 #include <executorch/runtime/platform/assert.h>
 
@@ -66,6 +67,117 @@ template <
     typename CTYPE_OUT>
 struct MulInner<false, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT>
     : public ReportCanCastBug {};
+
+Tensor& handle_last_dim_broadcast(
+    KernelRuntimeContext& ctx,
+    const Tensor& a,
+    const Tensor& b,
+    Tensor& out,
+    const ElementwiseOptimizedPath selected_optimized_path) {
+  ScalarType out_type = out.scalar_type();
+  const Tensor* lhs;
+  const Tensor* rhs;
+  if (selected_optimized_path ==
+      ElementwiseOptimizedPath::kBroadcastLastDimReverseArguments) {
+    lhs = &b;
+    rhs = &a;
+  } else {
+    lhs = &a;
+    rhs = &b;
+  }
+  auto error = resize_tensor(out, lhs->sizes());
+  ET_KERNEL_CHECK_MSG(
+      ctx,
+      error == Error::Ok,
+      InvalidArgument,
+      out,
+      "Failed to resize output tensor.");
+  const size_t outer_size = getLeadingDims(out, out.dim() - 1);
+  const auto broadcast_size = out.size(out.dim() - 1);
+  ET_SWITCH_REALB_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
+    using Vec = executorch::vec::Vectorized<CTYPE>;
+    executorch::vec::broadcasting_map_broadcast_last_dim<CTYPE>(
+        [](Vec x, Vec y) { return x * y; },
+        out.mutable_data_ptr<CTYPE>(),
+        lhs->const_data_ptr<CTYPE>(),
+        rhs->const_data_ptr<CTYPE>(),
+        outer_size,
+        broadcast_size);
+  });
+  return out;
+}
+
+Tensor& handle_broadcast_mul(
+    KernelRuntimeContext& ctx,
+    const Tensor& a,
+    const Tensor& b,
+    Tensor& out,
+    const ElementwiseOptimizedPath selected_optimized_path) {
+
+  if ((selected_optimized_path ==
+      ElementwiseOptimizedPath::kBroadcastLastDim) ||
+      (selected_optimized_path == ElementwiseOptimizedPath::kBroadcastLastDimReverseArguments)) {
+    return handle_last_dim_broadcast(
+        ctx, a, b, out, selected_optimized_path);
+  }
+
+  ScalarType out_type = out.scalar_type();
+  const Tensor* lhs;
+  const Tensor* rhs;
+  if ((selected_optimized_path ==
+       ElementwiseOptimizedPath::kBroadcast2dBy1dReverseArguments) ||
+      (selected_optimized_path ==
+       ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments)) {
+    lhs = &b;
+    rhs = &a;
+  } else {
+    // Catch failure to update logic when adding new broadcasting possibility.
+    ET_DCHECK(
+        (selected_optimized_path ==
+         ElementwiseOptimizedPath::kBroadcast2dBy1d) ||
+        (selected_optimized_path ==
+         ElementwiseOptimizedPath::kBroadcastNdByNd));
+    lhs = &a;
+    rhs = &b;
+  }
+  auto error = resize_tensor(out, lhs->sizes());
+  ET_KERNEL_CHECK_MSG(
+      ctx,
+      error == Error::Ok,
+      InvalidArgument,
+      out,
+      "Failed to resize output tensor.");
+  int64_t outer_size = 1;
+  int64_t broadcast_size;
+  int64_t inner_size;
+  if ((selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNd) ||
+      (selected_optimized_path ==
+       ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments)) {
+    int32_t broadcast_dim = internal::get_broadcast_dim(*lhs, *rhs);
+    int32_t broadcast_dim_lhs = lhs->dim() + broadcast_dim;
+    int32_t broadcast_dim_rhs = rhs->dim() + broadcast_dim;
+    auto normalized_tensor_size_lhs =
+        get_normalized_tensor_size(*lhs, broadcast_dim_lhs);
+    outer_size = normalized_tensor_size_lhs[0];
+    broadcast_size = normalized_tensor_size_lhs[1];
+    inner_size = normalized_tensor_size_lhs[2];
+  } else {
+    broadcast_size = lhs->sizes()[lhs->dim() - 2];
+    inner_size = lhs->sizes()[lhs->dim() - 1];
+  }
+  ET_SWITCH_REALB_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
+    using Vec = executorch::vec::Vectorized<CTYPE>;
+    executorch::vec::broadcasting_map_3d_and_unsqueezed_3d<CTYPE>(
+        [](Vec x, Vec y) { return x * y; },
+        out.mutable_data_ptr<CTYPE>(),
+        lhs->const_data_ptr<CTYPE>(),
+        rhs->const_data_ptr<CTYPE>(),
+        outer_size,
+        broadcast_size,
+        inner_size);
+  });
+  return out;
+}
 } // namespace
 
 Tensor& opt_mul_out(
@@ -128,56 +240,7 @@ Tensor& opt_mul_out(
           out.numel());
     });
   } else if (selected_optimized_path != ElementwiseOptimizedPath::kNone) {
-    const Tensor* lhs;
-    const Tensor* rhs;
-    if ((selected_optimized_path ==
-        ElementwiseOptimizedPath::kBroadcast2dBy1dReverseArguments) ||
-        (selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments)) {
-      lhs = &b;
-      rhs = &a;
-    } else {
-      // Catch failure to update logic when adding new broadcasting possibility.
-      ET_DCHECK(
-          (selected_optimized_path ==
-          ElementwiseOptimizedPath::kBroadcast2dBy1d) ||
-          (selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNd));
-      lhs = &a;
-      rhs = &b;
-    }
-    auto error = resize_tensor(out, lhs->sizes());
-    ET_KERNEL_CHECK_MSG(
-        ctx,
-        error == Error::Ok,
-        InvalidArgument,
-        out,
-        "Failed to resize output tensor.");
-    int64_t outer_size = 1;
-    int64_t broadcast_size;
-    int64_t inner_size;
-    if ((selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNd) ||
-        (selected_optimized_path == ElementwiseOptimizedPath::kBroadcastNdByNdReverseArguments)) {
-          int32_t broadcast_dim = internal::get_broadcast_dim(*lhs, *rhs);
-          int32_t broadcast_dim_lhs = lhs->dim() + broadcast_dim;
-          int32_t broadcast_dim_rhs = rhs->dim() + broadcast_dim;
-          auto normalized_tensor_size_lhs = get_normalized_tensor_size(*lhs, broadcast_dim_lhs);
-          outer_size = normalized_tensor_size_lhs[0];
-          broadcast_size = normalized_tensor_size_lhs[1];
-          inner_size = normalized_tensor_size_lhs[2];
-    } else {
-      broadcast_size = lhs->sizes()[lhs->dim() - 2];
-      inner_size = lhs->sizes()[lhs->dim() - 1];
-    }
-    ET_SWITCH_REALB_TYPES(out_type, ctx, "mul.out", CTYPE, [&]() {
-      using Vec = executorch::vec::Vectorized<CTYPE>;
-      executorch::vec::broadcasting_map_3d_and_unsqueezed_3d<CTYPE>(
-          [](Vec x, Vec y) { return x * y; },
-          out.mutable_data_ptr<CTYPE>(),
-          lhs->const_data_ptr<CTYPE>(),
-          rhs->const_data_ptr<CTYPE>(),
-          outer_size,
-          broadcast_size,
-          inner_size);
-    });
+    return handle_broadcast_mul(ctx, a, b, out, selected_optimized_path);
   } else {
     ScalarType common_type =
         promoteTypes(a_type, b_type, /*half_to_float*/ true);
diff --git a/kernels/optimized/cpu/targets.bzl b/kernels/optimized/cpu/targets.bzl
index 488d2af7fa1..77a270cc45d 100644
--- a/kernels/optimized/cpu/targets.bzl
+++ b/kernels/optimized/cpu/targets.bzl
@@ -72,6 +72,7 @@ _OPTIMIZED_ATEN_OPS = (
             ":binary_ops",
             "//executorch/kernels/portable/cpu:scalar_utils",
             "//executorch/kernels/portable/cpu/util:broadcast_util",
+            "//executorch/runtime/core/exec_aten/util:tensor_util",
         ],
     ),
     op_target(
diff --git a/kernels/optimized/vec/functional_base.h b/kernels/optimized/vec/functional_base.h
index 3a66904f8a9..a43007bfa4e 100644
--- a/kernels/optimized/vec/functional_base.h
+++ b/kernels/optimized/vec/functional_base.h
@@ -378,5 +378,34 @@ inline void broadcasting_map_2d_by_1d(
   broadcasting_map_3d_and_unsqueezed_3d(vec_fun, output_data, input_data, input_data2, 1, size, size2);
 }
 
+template <typename scalar_t, typename Op>
+inline void broadcasting_map_broadcast_last_dim(
+    const Op& vec_fun,
+    scalar_t* output_data,
+    const scalar_t* lhs,
+    const scalar_t* rhs,
+    int64_t outer_size,
+    int64_t broadcast_size) {
+  using Vec = vec::Vectorized<scalar_t>;
+  int64_t outer_stride_lhs = broadcast_size;
+  int64_t outer_stride_rhs = 1;
+  for (int64_t outer_idx = 0; outer_idx < outer_size; ++outer_idx) {
+    const scalar_t* lhs_outer = lhs + outer_idx * outer_stride_lhs;
+    scalar_t* output_data_row = output_data + outer_idx * outer_stride_lhs;
+    int64_t inner_idx = 0;
+    Vec data_vec2 = Vec(rhs[outer_idx]);
+    for (; inner_idx < broadcast_size - (broadcast_size % Vec::size()); inner_idx += Vec::size()) {
+      Vec data_vec = Vec::loadu(lhs_outer + inner_idx);
+      Vec output_vec = vec_fun(data_vec, data_vec2);
+      output_vec.store(output_data_row + inner_idx);
+    }
+    if (broadcast_size - inner_idx > 0) {
+      Vec data_vec = Vec::loadu(lhs_outer + inner_idx, broadcast_size - inner_idx);
+      Vec output_vec = vec_fun(data_vec, data_vec2);
+      output_vec.store(output_data_row + inner_idx, broadcast_size - inner_idx);
+    }
+  }
+}
+
 } // namespace vec
 } // namespace executorch