diff --git a/cmake/onnxruntime_unittests.cmake b/cmake/onnxruntime_unittests.cmake
index a4ba85e868896..f6ace371531f9 100644
--- a/cmake/onnxruntime_unittests.cmake
+++ b/cmake/onnxruntime_unittests.cmake
@@ -1128,7 +1128,8 @@ if (NOT onnxruntime_ENABLE_TRAINING_TORCH_INTEROP)
       ${BENCHMARK_DIR}/gelu.cc
       ${BENCHMARK_DIR}/activation.cc
       ${BENCHMARK_DIR}/quantize.cc
-      ${BENCHMARK_DIR}/reduceminmax.cc)
+      ${BENCHMARK_DIR}/reduceminmax.cc
+      ${BENCHMARK_DIR}/layer_normalization.cc)
     target_include_directories(onnxruntime_benchmark PRIVATE ${ONNXRUNTIME_ROOT} ${onnxruntime_graph_header} ${ONNXRUNTIME_ROOT}/core/mlas/inc)
     target_compile_definitions(onnxruntime_benchmark PRIVATE BENCHMARK_STATIC_DEFINE)
     if(WIN32)
diff --git a/onnxruntime/contrib_ops/cpu/skip_layer_norm.cc b/onnxruntime/contrib_ops/cpu/skip_layer_norm.cc
index faf78cae80ee1..67b4950af73bf 100644
--- a/onnxruntime/contrib_ops/cpu/skip_layer_norm.cc
+++ b/onnxruntime/contrib_ops/cpu/skip_layer_norm.cc
@@ -2,6 +2,7 @@
 // Licensed under the MIT License.
 
 #include "core/framework/tensor.h"
+#include "core/mlas/inc/mlas.h"
 #include "core/util/math_cpuonly.h"
 #include "core/providers/common.h"
 #include "core/platform/threadpool.h"
@@ -36,52 +37,188 @@ REGISTER_KERNEL_TYPED(float)
 REGISTER_KERNEL_TYPED(double)
 REGISTER_KERNEL_TYPED(MLFloat16)
 
-// Utility to convert from MLFloat16 to float only when the input type is MLFloat16.
-template <typename T, typename Ret>
-ORT_FORCEINLINE Ret ConvertMLFloat16ToDoubleOrFloatIfNeeded(T val);
-
-template <>
-ORT_FORCEINLINE float ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, float>(MLFloat16 val) {
-  return val.ToFloat();
-}
-
-template <>
-ORT_FORCEINLINE double ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, double>(MLFloat16 val) {
-  return static_cast<double>(ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, float>(val));
+namespace {
+
+template <typename T, typename = std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double>, void>>
+void ComputeJob(
+    const T* input_data,
+    const T* skip_data,
+    const T* gamma_data,
+    const T* beta_data,
+    const T* bias_data,
+    IAllocatorUniquePtr<float>& skip_float_uptr,
+    IAllocatorUniquePtr<float>& gamma_float_uptr,
+    IAllocatorUniquePtr<float>& beta_float_uptr,
+    IAllocatorUniquePtr<float>& bias_float_uptr,
+    ptrdiff_t task_idx,
+    int hidden_size,
+    int64_t skip_size,
+    float epsilon,
+    bool simplified,
+    T* output_data,
+    T* skip_input_bias_add_output_data,
+    AllocatorPtr alloc) {
+  ORT_UNUSED_PARAMETER(skip_float_uptr);   // only used in MLFloat16 overload
+  ORT_UNUSED_PARAMETER(gamma_float_uptr);  // only used in MLFloat16 overload
+  ORT_UNUSED_PARAMETER(beta_float_uptr);   // only used in MLFloat16 overload
+  ORT_UNUSED_PARAMETER(bias_float_uptr);   // only used in MLFloat16 overload
+  ORT_UNUSED_PARAMETER(alloc);
+
+  auto offset = task_idx * hidden_size;
+  const T* p_input = input_data + offset;
+  const T* p_skip = skip_data + (offset % skip_size);
+  T* p_output = output_data + offset;
+  T* p_skip_input_bias_add_output = skip_input_bias_add_output_data == nullptr ? nullptr : skip_input_bias_add_output_data + offset;
+
+  T mean(0.0f);
+  T mean_square(0.0f);
+
+  for (decltype(hidden_size) h = 0; h < hidden_size; h++) {
+    T val = p_input[h] + p_skip[h];
+
+    if (nullptr != bias_data) {
+      val += bias_data[h];
+    }
+
+    if (nullptr != p_skip_input_bias_add_output) {
+      p_skip_input_bias_add_output[h] = val;
+    }
+
+    p_output[h] = val;
+    mean += val;
+    mean_square += val * val;
+  }
+
+  mean = mean / hidden_size;
+  if (simplified) {
+    mean_square = sqrt(mean_square / hidden_size + epsilon);
+  } else {
+    mean_square = sqrt(mean_square / hidden_size - mean * mean + epsilon);
+  }
+
+  for (decltype(hidden_size) h = 0; h < hidden_size; h++) {
+    if (simplified) {
+      p_output[h] = p_output[h] / mean_square * gamma_data[h];
+    } else if (nullptr == beta_data) {
+      p_output[h] = (p_output[h] - mean) / mean_square * gamma_data[h];
+    } else {
+      p_output[h] = (p_output[h] - mean) / mean_square * gamma_data[h] + beta_data[h];
+    }
+  }
 }
 
-template <>
-ORT_FORCEINLINE constexpr float ConvertMLFloat16ToDoubleOrFloatIfNeeded<float, float>(float val) {
-  return val;
+void ComputeJob(
+    const MLFloat16* input_data,
+    const MLFloat16* skip_data,
+    const MLFloat16* gamma_data,
+    const MLFloat16* beta_data,
+    const MLFloat16* bias_data,
+    IAllocatorUniquePtr<float>& skip_float_uptr,
+    IAllocatorUniquePtr<float>& gamma_float_uptr,
+    IAllocatorUniquePtr<float>& beta_float_uptr,
+    IAllocatorUniquePtr<float>& bias_float_uptr,
+    ptrdiff_t task_idx,
+    int hidden_size,
+    int64_t skip_size,
+    float epsilon,
+    bool simplified,
+    MLFloat16* output_data,
+    MLFloat16* skip_input_bias_add_output_data,
+    AllocatorPtr alloc) {
+  auto offset = task_idx * hidden_size;
+  const MLFloat16* p_input = input_data + offset;
+  const MLFloat16* p_skip = skip_data + (offset % skip_size);
+  MLFloat16* p_output = output_data + offset;
+  MLFloat16* p_skip_input_bias_add_output = skip_input_bias_add_output_data == nullptr ? nullptr : skip_input_bias_add_output_data + offset;
+
+  float mean(0.0f);
+  float mean_square(0.0f);
+  const size_t num_elems = static_cast<size_t>(hidden_size);
+
+  IAllocatorUniquePtr<float> input_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+  MlasConvertHalfToFloatBuffer(p_input, input_float_uptr.get(), num_elems);
+
+  if (!skip_float_uptr) {
+    skip_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+    MlasConvertHalfToFloatBuffer(p_skip, skip_float_uptr.get(), num_elems);
+  }
+
+  if (bias_data && !bias_float_uptr) {
+    bias_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+    MlasConvertHalfToFloatBuffer(bias_data, bias_float_uptr.get(), num_elems);
+  }
+
+  IAllocatorUniquePtr<float> output_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+  float* output_float_ptr = output_float_uptr.get();
+
+  const float* input_float_ptr = input_float_uptr.get();
+  const float* skip_float_ptr = skip_float_uptr.get();
+  const float* bias_float_ptr = bias_float_uptr.get();
+  for (size_t h = 0; h < num_elems; h++) {
+    float val = input_float_ptr[h] + skip_float_ptr[h];
+
+    if (bias_float_uptr) {
+      val += bias_float_ptr[h];
+    }
+
+    output_float_ptr[h] = val;
+    mean += val;
+    mean_square += val * val;
+  }
+
+  if (nullptr != p_skip_input_bias_add_output) {
+    MlasConvertFloatToHalfBuffer(output_float_ptr, p_skip_input_bias_add_output, num_elems);
+  }
+
+  mean = mean / hidden_size;
+  if (simplified) {
+    mean_square = sqrt(mean_square / hidden_size + epsilon);
+  } else {
+    mean_square = sqrt(mean_square / hidden_size - mean * mean + epsilon);
+  }
+
+  if (!gamma_float_uptr) {
+    gamma_float_uptr = std::move(input_float_uptr);  // overwrite input with gamma values, since they have the same size
+    MlasConvertHalfToFloatBuffer(gamma_data, gamma_float_uptr.get(), num_elems);
+  }
+
+  if (beta_data && !beta_float_uptr) {
+    beta_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+    MlasConvertHalfToFloatBuffer(beta_data, beta_float_uptr.get(), num_elems);
+  }
+
+  const float* gamma_float_ptr = gamma_float_uptr.get();
+  const float* beta_float_ptr = beta_float_uptr.get();
+  for (size_t h = 0; h < num_elems; h++) {
+    if (simplified) {
+      output_float_ptr[h] = output_float_ptr[h] / mean_square * gamma_float_ptr[h];
+    } else if (nullptr == beta_float_uptr) {
+      output_float_ptr[h] = (output_float_ptr[h] - mean) / mean_square * gamma_float_ptr[h];
+    } else {
+      output_float_ptr[h] = (output_float_ptr[h] - mean) / mean_square * gamma_float_ptr[h] + beta_float_ptr[h];
+    }
+  }
+
+  MlasConvertFloatToHalfBuffer(output_float_ptr, p_output, num_elems);
 }
 
-template <>
-ORT_FORCEINLINE constexpr double ConvertMLFloat16ToDoubleOrFloatIfNeeded<double, double>(double val) {
-  return val;
-}
+void ConvertMLFloat16ToFloatIfNeeded(const Tensor& tensor, AllocatorPtr alloc, IAllocatorUniquePtr<float>& dest, bool& is_packed) {
+  if (tensor.GetElementType() == utils::ToTensorProtoElementType<MLFloat16>()) {
+    auto tensor_data_ptr = tensor.Data<MLFloat16>();
+    auto tensor_size = static_cast<size_t>(tensor.Shape().Size());
+    auto float_ptr = IAllocator::MakeUniquePtr<float>(alloc, tensor_size, true);
 
-// Function template that only converts the input value to MLFloat16 if T is MLFloat16.
-template <typename T>
-ORT_FORCEINLINE constexpr typename std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double>, T>
-ConvertDoubleOrFloatToMLFloat16IfNeeded(T val) {
-  return val;
+    MlasConvertHalfToFloatBuffer(tensor_data_ptr, float_ptr.get(), tensor_size);
+    dest = std::move(float_ptr);
+    is_packed = true;
+  }
 }
 
-template <typename T>
-ORT_FORCEINLINE constexpr typename std::enable_if_t<std::is_same_v<T, MLFloat16>, T>
-ConvertDoubleOrFloatToMLFloat16IfNeeded(float val) {
-  return MLFloat16(val);
-}
-
-template <typename T>
-ORT_FORCEINLINE constexpr typename std::enable_if_t<std::is_same_v<T, MLFloat16>, T>
-ConvertDoubleOrFloatToMLFloat16IfNeeded(double val) {
-  return MLFloat16(static_cast<float>(val));
-}
+}  // namespace
 
 template <typename T, bool simplified>
 SkipLayerNorm<T, simplified>::SkipLayerNorm(const OpKernelInfo& op_kernel_info)
-    : OpKernel(op_kernel_info) {
+    : OpKernel(op_kernel_info), skip_fp32_(nullptr), gamma_fp32_(nullptr), beta_fp32_(nullptr), bias_fp32_(nullptr) {
   ORT_ENFORCE(op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK());
   ORT_ENFORCE(epsilon_ >= 0);
 }
@@ -94,8 +231,7 @@ Status SkipLayerNorm<T, simplified>::Compute(OpKernelContext* p_ctx) const {
   const Tensor* beta = p_ctx->Input<Tensor>(3);
   const Tensor* bias = p_ctx->Input<Tensor>(4);
   Tensor* output = p_ctx->Output(0, input->Shape());
-  // For inferencing, we support one more optional output which is the sum
-  // of the input and skip tensors
+  // For inferencing, we support one more optional output which is the sum of the input and skip tensors
   Tensor* skip_input_bias_add_output = p_ctx->Output(3, input->Shape());
 
   const auto& input_dims = input->Shape().GetDims();
@@ -120,75 +256,44 @@ Status SkipLayerNorm<T, simplified>::Compute(OpKernelContext* p_ctx) const {
 
   T* output_data = output->MutableData<T>();
 
-  // For inferencing, we support one more optional output which is the sum
-  // of the input and skip tensors
-  T* skip_input_bias_add_output_data = skip_input_bias_add_output != nullptr ? skip_input_bias_add_output->MutableData<T>() : nullptr;
+  // For inferencing, we support one more optional output which is the sum of the input and skip tensors
+  T* skip_input_bias_add_output_data = skip_input_bias_add_output == nullptr ? nullptr : skip_input_bias_add_output->MutableData<T>();
 
-  const auto& skip_size = skip->Shape().Size();
+  const int64_t& skip_size = skip->Shape().Size();
+
+  AllocatorPtr alloc;
+  ORT_RETURN_IF_ERROR(p_ctx->GetTempSpaceAllocator(&alloc));
 
   concurrency::ThreadPool::TryBatchParallelFor(
       p_ctx->GetOperatorThreadPool(), static_cast<int32_t>(task_count),
       [&](ptrdiff_t task_idx) {
-        auto offset = task_idx * hidden_size;
-
-        const T* p_input = input_data + offset;
-        const T* p_skip = skip_data + (offset % skip_size);
-        T* p_output = output_data + offset;
-        T* p_skip_input_bias_add_output_data = skip_input_bias_add_output_data != nullptr ? skip_input_bias_add_output_data + offset : nullptr;
-
-        using DoubleOrFloat = typename std::conditional<
-            std::is_same<T, double>::value,  // If T is double
-            double,                          // Use double
-            float                            // Otherwise, use float (covers float and MLFloat16)
-            >::type;
-
-        DoubleOrFloat mean(0.0f);
-        DoubleOrFloat mean_square(0.0f);
-
-        std::unique_ptr<DoubleOrFloat[]> output_buffer = std::make_unique<DoubleOrFloat[]>(hidden_size);
-        for (size_t h = 0; h < static_cast<size_t>(hidden_size); h++) {
-          DoubleOrFloat input_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(p_input[h]);
-          DoubleOrFloat skip_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(p_skip[h]);
-
-          DoubleOrFloat value = input_value + skip_value;
-
-          if (nullptr != bias_data) {
-            value += ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(bias_data[h]);
-          }
-
-          output_buffer[h] = value;
-          T converted_value = ConvertDoubleOrFloatToMLFloat16IfNeeded<T>(value);
-          if (nullptr != p_skip_input_bias_add_output_data) {
-            p_skip_input_bias_add_output_data[h] = converted_value;
-          }
-
-          mean += value;
-          mean_square += value * value;
-        }
-
-        mean = mean / hidden_size;
-        if (simplified) {
-          mean_square = sqrt(mean_square / hidden_size + epsilon_);
-        } else {
-          mean_square = sqrt(mean_square / hidden_size - mean * mean + epsilon_);
-        }
-
-        for (size_t h = 0; h < static_cast<size_t>(hidden_size); h++) {
-          DoubleOrFloat gamma_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(gamma_data[h]);
-          if (simplified) {
-            p_output[h] = ConvertDoubleOrFloatToMLFloat16IfNeeded<T>(output_buffer[h] / mean_square * gamma_value);
-          } else if (nullptr == beta_data) {
-            p_output[h] = ConvertDoubleOrFloatToMLFloat16IfNeeded<T>((output_buffer[h] - mean) / mean_square * gamma_value);
-          } else {
-            DoubleOrFloat beta_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(beta_data[h]);
-            p_output[h] = ConvertDoubleOrFloatToMLFloat16IfNeeded<T>((output_buffer[h] - mean) / mean_square * gamma_value + beta_value);
-          }
-        }
+        ComputeJob(input_data, skip_data, gamma_data, beta_data, bias_data, skip_fp32_, gamma_fp32_, beta_fp32_,
+                   bias_fp32_, task_idx, hidden_size, skip_size, epsilon_, simplified, output_data,
+                   skip_input_bias_add_output_data, alloc);
       },
       0);
 
   return Status::OK();
 }
 
+template <typename T, bool simplified>
+Status SkipLayerNorm<T, simplified>::PrePack(const Tensor& tensor, int input_idx, AllocatorPtr alloc,
+                                             bool& is_packed, PrePackedWeights* prepacked_weights) {
+  ORT_UNUSED_PARAMETER(prepacked_weights);
+
+  is_packed = false;
+  if (input_idx == 1) {  // skip
+    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, skip_fp32_, is_packed);
+  } else if (input_idx == 2) {  // gamma
+    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, gamma_fp32_, is_packed);
+  } else if (input_idx == 3) {  // beta
+    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, beta_fp32_, is_packed);
+  } else if (input_idx == 4) {  // bias
+    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, bias_fp32_, is_packed);
+  }
+
+  return Status::OK();
+}
+
 }  // namespace contrib
 }  // namespace onnxruntime
diff --git a/onnxruntime/contrib_ops/cpu/skip_layer_norm.h b/onnxruntime/contrib_ops/cpu/skip_layer_norm.h
index 69edf4609e340..08e2276c3d9d5 100644
--- a/onnxruntime/contrib_ops/cpu/skip_layer_norm.h
+++ b/onnxruntime/contrib_ops/cpu/skip_layer_norm.h
@@ -16,8 +16,15 @@ class SkipLayerNorm final : public OpKernel {
   SkipLayerNorm(const OpKernelInfo& op_kernel_info);
   Status Compute(OpKernelContext* p_op_kernel_context) const override;
 
+  Status PrePack(const Tensor& tensor, int input_idx, AllocatorPtr alloc,
+                 bool& is_packed, PrePackedWeights* prepacked_weights) override;
+
  private:
   float epsilon_;
+  mutable IAllocatorUniquePtr<float> skip_fp32_;
+  mutable IAllocatorUniquePtr<float> gamma_fp32_;
+  mutable IAllocatorUniquePtr<float> beta_fp32_;
+  mutable IAllocatorUniquePtr<float> bias_fp32_;
 };
 
 }  // namespace contrib
diff --git a/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cc b/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cc
index 23630dcb63efa..f73efcddcedd4 100644
--- a/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cc
+++ b/onnxruntime/core/providers/cpu/nn/layer_norm_impl.cc
@@ -5,6 +5,7 @@
 
 #include "core/common/safeint.h"
 #include "core/framework/tensor.h"
+#include "core/mlas/inc/mlas.h"
 #include "core/platform/threadpool.h"
 #include "core/providers/common.h"
 #include "core/util/force_inline.h"
@@ -12,66 +13,166 @@
 
 namespace onnxruntime {
 
-// Utility to convert from MLFloat16 to float only when the input type is MLFloat16.
-template <typename T, typename Ret>
-ORT_FORCEINLINE Ret ConvertMLFloat16ToDoubleOrFloatIfNeeded(T val);
+namespace {
 
-template <>
-ORT_FORCEINLINE float ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, float>(MLFloat16 val) {
-  return val.ToFloat();
-}
+template <typename T,
+          typename U,
+          typename = std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double>, void>>
+void ComputeJob(
+    const T* X_data,
+    const T* scale_data,
+    const T* bias_data,
+    const ptrdiff_t task_idx,
+    const int64_t norm_size,
+    IAllocatorUniquePtr<float>& scale_float_uptr,
+    IAllocatorUniquePtr<float>& bias_float_uptr,
+    float epsilon,
+    bool simplified,
+    T* Y_data,
+    U* mean_data,
+    U* inv_std_dev_data,
+    AllocatorPtr alloc) {
+  ORT_UNUSED_PARAMETER(scale_float_uptr);  // only used in MLFloat16 overload
+  ORT_UNUSED_PARAMETER(bias_float_uptr);   // only used in MLFloat16 overload
+  ORT_UNUSED_PARAMETER(alloc);
+
+  const T* p_input = X_data + task_idx * norm_size;
+  T* p_output = Y_data + task_idx * norm_size;
+
+  T mean(0.0f);
+  T mean_square(0.0f);
+
+  for (int64_t h = 0; h < norm_size; h++) {
+    p_output[h] = p_input[h];
+    mean += p_input[h];
+    mean_square += p_input[h] * p_input[h];
+  }
 
-template <>
-ORT_FORCEINLINE double ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, double>(MLFloat16 val) {
-  return double(ConvertMLFloat16ToDoubleOrFloatIfNeeded<MLFloat16, float>(val));
-}
+  mean = mean / norm_size;
+  if (simplified) {
+    mean_square = sqrt(mean_square / norm_size + epsilon);
+  } else {
+    mean_square = sqrt(mean_square / norm_size - mean * mean + epsilon);
+  }
 
-template <>
-ORT_FORCEINLINE constexpr float ConvertMLFloat16ToDoubleOrFloatIfNeeded<float, float>(float val) {
-  return val;
-}
+  for (int64_t h = 0; h < norm_size; h++) {
+    if (simplified) {
+      p_output[h] = p_output[h] / mean_square * scale_data[h];
+    } else if (nullptr == bias_data) {
+      p_output[h] = (p_output[h] - mean) / mean_square * scale_data[h];
+    } else {
+      p_output[h] = (p_output[h] - mean) / mean_square * scale_data[h] + bias_data[h];
+    }
+  }
 
-template <>
-ORT_FORCEINLINE constexpr double ConvertMLFloat16ToDoubleOrFloatIfNeeded<double, double>(double val) {
-  return val;
-}
+  if (mean_data != nullptr) {
+    // ONNX spec doesn't support 'double' for 'U' so when 'T' == double, 'U' == float and we need to narrow
+    mean_data[task_idx] = gsl::narrow_cast<float>(mean);
+  }
 
-ORT_FORCEINLINE constexpr float ConvertToFloatIfNeeded(float val) {
-  return val;
+  if (inv_std_dev_data != nullptr) {
+    inv_std_dev_data[task_idx] = gsl::narrow_cast<float>(1 / mean_square);
+  }
 }
 
-ORT_FORCEINLINE constexpr float ConvertToFloatIfNeeded(double val) {
-  // ONNX spec doesn't support 'double' for 'Ret' so when 'T' == double, 'Ret' == float and we need to narrow
-  return gsl::narrow_cast<float>(val);
-}
+template <typename U>
+void ComputeJob(
+    const MLFloat16* X_data,
+    const MLFloat16* scale_data,
+    const MLFloat16* bias_data,
+    const ptrdiff_t task_idx,
+    const int64_t norm_size,
+    IAllocatorUniquePtr<float>& scale_float_uptr,
+    IAllocatorUniquePtr<float>& bias_float_uptr,
+    float epsilon,
+    bool simplified,
+    MLFloat16* Y_data,
+    U* mean_data,
+    U* inv_std_dev_data,
+    AllocatorPtr alloc) {
+  const MLFloat16* p_input = X_data + task_idx * norm_size;
+  MLFloat16* p_output = Y_data + task_idx * norm_size;
+
+  float mean(0.0f);
+  float mean_square(0.0f);
+
+  const size_t num_elems = static_cast<size_t>(norm_size);
+  IAllocatorUniquePtr<float> input_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+  MlasConvertHalfToFloatBuffer(p_input, input_float_uptr.get(), num_elems);
+
+  IAllocatorUniquePtr<float> output_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+  float* output_float_ptr = output_float_uptr.get();
+
+  const float* input_float_ptr = input_float_uptr.get();
+  for (size_t h = 0; h < num_elems; h++) {
+    output_float_ptr[h] = input_float_ptr[h];
+    mean += input_float_ptr[h];
+    mean_square += input_float_ptr[h] * input_float_ptr[h];
+  }
 
-// Function template that only converts the input value to MLFloat16 if T is MLFloat16.
-template <typename T>
-ORT_FORCEINLINE constexpr typename std::enable_if_t<std::is_same_v<T, float> || std::is_same_v<T, double>, float>
-ConvertToMLFloat16IfNeeded(float val) {
-  return val;
-}
+  mean = mean / norm_size;
+  if (simplified) {
+    mean_square = sqrt(mean_square / norm_size + epsilon);
+  } else {
+    mean_square = sqrt(mean_square / norm_size - mean * mean + epsilon);
+  }
 
-template <typename T>
-ORT_FORCEINLINE constexpr typename std::enable_if_t<std::is_same_v<T, MLFloat16>, MLFloat16>
-ConvertToMLFloat16IfNeeded(float val) {
-  return MLFloat16(val);
+  if (!scale_float_uptr) {
+    scale_float_uptr = std::move(input_float_uptr);  // overwrite input with scale values, since they have the same size
+    MlasConvertHalfToFloatBuffer(scale_data, scale_float_uptr.get(), num_elems);
+  }
+
+  if (bias_data && !bias_float_uptr) {
+    bias_float_uptr = IAllocator::MakeUniquePtr<float>(alloc, num_elems);
+    MlasConvertHalfToFloatBuffer(bias_data, bias_float_uptr.get(), num_elems);
+  }
+
+  const float* scale_float_ptr = scale_float_uptr.get();
+  const float* bias_float_ptr = bias_float_uptr.get();
+  for (size_t h = 0; h < num_elems; h++) {
+    if (simplified) {
+      output_float_ptr[h] = output_float_ptr[h] / mean_square * scale_float_ptr[h];
+    } else if (nullptr == bias_data) {
+      output_float_ptr[h] = (output_float_ptr[h] - mean) / mean_square * scale_float_ptr[h];
+    } else {
+      output_float_ptr[h] = (output_float_ptr[h] - mean) / mean_square * scale_float_ptr[h] + bias_float_ptr[h];
+    }
+  }
+
+  MlasConvertFloatToHalfBuffer(output_float_ptr, p_output, num_elems);
+
+  if (mean_data != nullptr) {
+    // ONNX spec doesn't support 'double' for 'U' so when 'T' == double, 'U' == float and we need to narrow
+    mean_data[task_idx] = MLFloat16(mean);
+  }
+
+  if (inv_std_dev_data != nullptr) {
+    inv_std_dev_data[task_idx] = MLFloat16(1 / mean_square);
+  }
 }
 
-template <typename T>
-ORT_FORCEINLINE constexpr double ConvertToMLFloat16IfNeeded(double val) {
-  return val;
+void ConvertMLFloat16ToFloatIfNeeded(const Tensor& tensor, AllocatorPtr alloc, IAllocatorUniquePtr<float>& dest, bool& is_packed) {
+  if (tensor.GetElementType() == utils::ToTensorProtoElementType<MLFloat16>()) {
+    auto tensor_data_ptr = tensor.Data<MLFloat16>();
+    auto tensor_size = static_cast<size_t>(tensor.Shape().Size());
+    auto float_ptr = IAllocator::MakeUniquePtr<float>(alloc, tensor_size, true);
+
+    MlasConvertHalfToFloatBuffer(tensor_data_ptr, float_ptr.get(), tensor_size);
+    dest = std::move(float_ptr);
+    is_packed = true;
+  }
 }
 
+}  // namespace
+
 LayerNormImpl::LayerNormImpl(const OpKernelInfo& op_kernel_info, bool simplified, bool contrib_op)
-    : OpKernel(op_kernel_info), simplified_{simplified}, contrib_op_{contrib_op} {
+    : OpKernel(op_kernel_info), simplified_{simplified}, contrib_op_{contrib_op}, scale_fp32_(nullptr), bias_fp32_(nullptr) {
   ORT_ENFORCE(op_kernel_info.GetAttr("axis", &axis_).IsOK());
   ORT_ENFORCE(op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK());
 }
 
-namespace {
 template <typename T, typename U>
-Status ComputeImpl(OpKernelContext* p_ctx, int64_t orig_axis, float epsilon, bool simplified) {
+Status LayerNormImpl::ComputeImpl(OpKernelContext* p_ctx, int64_t orig_axis, float epsilon, bool simplified) const {
   // Inputs
   const Tensor* X = p_ctx->Input<Tensor>(0);
   const Tensor* scale = p_ctx->Input<Tensor>(1);
@@ -81,21 +182,12 @@ Status ComputeImpl(OpKernelContext* p_ctx, int64_t orig_axis, float epsilon, boo
   const T* bias_data = (simplified || nullptr == bias) ? nullptr : bias->Data<T>();
 
   const TensorShape& x_shape = X->Shape();
-  const int64_t axis = HandleNegativeAxis(orig_axis, x_shape.NumDimensions());
-  int64_t norm_count = x_shape.SizeToDimension(onnxruntime::narrow<size_t>(axis));
-  int64_t norm_size = x_shape.SizeFromDimension(onnxruntime::narrow<size_t>(axis));
-
-  const auto scale_size = scale->Shape().Size();
-  const auto bias_size = (bias_data) ? bias->Shape().Size() : 0;
-  if (scale_size != norm_size || (bias_data && bias_size != norm_size)) {
-    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-                           "Size of X.shape()[axis:] == ", norm_size,
-                           ". Size of scale and bias (if provided) must match this. Got scale size of ",
-                           scale_size, " and bias size of ", bias_size);
-  }
-
+  const TensorShape& scale_shape = scale->Shape();
+  const TensorShape& bias_shape = bias->Shape();
   Tensor* Y = p_ctx->Output(0, x_shape);
-  auto Y_data = Y->MutableData<T>();
+  T* Y_data = Y->MutableData<T>();
+
+  const int64_t axis = HandleNegativeAxis(orig_axis, x_shape.NumDimensions());
 
   std::vector<int64_t> mean_inv_std_dev_dim;
   mean_inv_std_dev_dim.reserve(x_shape.NumDimensions());
@@ -107,11 +199,7 @@ Status ComputeImpl(OpKernelContext* p_ctx, int64_t orig_axis, float epsilon, boo
     }
   }
 
-  AllocatorPtr alloc;
-  ORT_RETURN_IF_ERROR(p_ctx->GetTempSpaceAllocator(&alloc));
-
   int output_index = 1;
-
   U* mean_data = nullptr;
   if (!simplified) {
     Tensor* mean = p_ctx->Output(output_index++, TensorShape(mean_inv_std_dev_dim));
@@ -126,87 +214,74 @@ Status ComputeImpl(OpKernelContext* p_ctx, int64_t orig_axis, float epsilon, boo
     inv_std_dev_data = inv_std_dev->MutableData<U>();
   }
 
-  concurrency::ThreadPool::TryBatchParallelFor(
-      p_ctx->GetOperatorThreadPool(), static_cast<int32_t>(norm_count),
-      [&](ptrdiff_t task_idx) {
-        const T* p_input = X_data + task_idx * norm_size;
-        T* p_output = Y_data + task_idx * norm_size;
-
-        using DoubleOrFloat = typename std::conditional<
-            std::is_same<T, double>::value,  // If T is double
-            double,                          // Use double
-            float                            // Otherwise, use float (covers float and MLFloat16)
-            >::type;
-
-        DoubleOrFloat mean(0.0f);
-        DoubleOrFloat mean_square(0.0f);
-
-        for (int64_t h = 0; h < norm_size; h++) {
-          DoubleOrFloat input_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(p_input[h]);
-          mean += input_value;
-          mean_square += input_value * input_value;
-        }
-
-        mean = mean / norm_size;
-        if (simplified) {
-          mean_square = sqrt(mean_square / norm_size + epsilon);
-        } else {
-          mean_square = sqrt(mean_square / norm_size - mean * mean + epsilon);
-        }
-
-        for (int64_t h = 0; h < norm_size; h++) {
-          DoubleOrFloat input_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(p_input[h]);
-          DoubleOrFloat scale_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(scale_data[h]);
-          if (simplified) {
-            p_output[h] = ConvertToMLFloat16IfNeeded<T>(input_value / mean_square * scale_value);
-          } else if (nullptr == bias) {
-            p_output[h] = ConvertToMLFloat16IfNeeded<T>((input_value - mean) / mean_square * scale_value);
-          } else {
-            DoubleOrFloat bias_value = ConvertMLFloat16ToDoubleOrFloatIfNeeded<T, DoubleOrFloat>(bias_data[h]);
-            p_output[h] = ConvertToMLFloat16IfNeeded<T>((input_value - mean) / mean_square * scale_value + bias_value);
-          }
-        }
-
-        if (mean_data != nullptr) {
-          // ONNX spec doesn't support 'double' for 'U' so when 'T' == double, 'U' == float and we need to narrow
-          mean_data[task_idx] = ConvertToMLFloat16IfNeeded<U>(ConvertToFloatIfNeeded(mean));
-        }
-
-        if (inv_std_dev_data != nullptr) {
-          inv_std_dev_data[task_idx] = ConvertToMLFloat16IfNeeded<U>(ConvertToFloatIfNeeded(1 / mean_square));
-        }
-      },
-      0);
+  onnxruntime::concurrency::ThreadPool* thread_pool = p_ctx->GetOperatorThreadPool();
 
-  return Status::OK();
+  AllocatorPtr alloc;
+  ORT_RETURN_IF_ERROR(p_ctx->GetTempSpaceAllocator(&alloc));
+  return ComputeWithoutContext<T, U>(X_data, x_shape, scale_data, scale_shape, bias_data, bias_shape, Y_data, mean_data,
+                                     inv_std_dev_data, thread_pool, axis, epsilon, simplified, alloc);
 }
 
-template <typename T>
-struct SrcDispatcher {
-  Status operator()(OpKernelContext* p_ctx, int64_t orig_axis, float epsilon, bool simplified, bool contrib_op) const {
-    // the contrib op kernel was always registered with the same type for all constraints.
-    // our implementation of the onnx op only supports 'float' as the U constraint.
-#if !defined(DISABLE_CONTRIB_OPS)
-    if (contrib_op) {
-      return ComputeImpl<T, T>(p_ctx, orig_axis, epsilon, simplified);
-    } else
-#else
-    ORT_UNUSED_PARAMETER(contrib_op);
-#endif
-    {
-      return ComputeImpl<T, float>(p_ctx, orig_axis, epsilon, simplified);
-    }
-  }
-};
-}  // namespace
-
 Status LayerNormImpl::Compute(OpKernelContext* p_ctx) const {
   const auto elem_type = p_ctx->Input<Tensor>(0)->GetElementType();
 
   using SupportedTypeList = boost::mp11::mp_list<float, double, MLFloat16>;
 
   utils::MLTypeCallDispatcherFromTypeList<SupportedTypeList> t_disp(elem_type);
-  return t_disp.InvokeRet<Status, SrcDispatcher>(p_ctx, axis_, epsilon_, simplified_, contrib_op_);
+  return t_disp.InvokeRet<Status, SrcDispatcher>(this, p_ctx, axis_, epsilon_, simplified_, contrib_op_);
+}
+
+Status LayerNormImpl::PrePack(const Tensor& tensor, int input_idx, AllocatorPtr alloc,
+                              bool& is_packed, PrePackedWeights* prepacked_weights) {
+  ORT_UNUSED_PARAMETER(prepacked_weights);
+
+  is_packed = false;
+  if (input_idx == 1) {  // scale
+    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, scale_fp32_, is_packed);
+  } else if (input_idx == 2) {  // bias
+    ConvertMLFloat16ToFloatIfNeeded(tensor, alloc, bias_fp32_, is_packed);
+  }
+
+  return Status::OK();
+}
+
+template <typename T, typename U>
+Status LayerNormImpl::ComputeWithoutContext(
+    const T* X_data,
+    const TensorShape& x_shape,
+    const T* scale_data,
+    const TensorShape& scale_shape,
+    const T* bias_data,
+    const TensorShape& bias_shape,
+    T* Y_data,
+    U* mean_data,
+    U* inv_std_dev_data,
+    onnxruntime::concurrency::ThreadPool* thread_pool,
+    int64_t axis,
+    float epsilon,
+    bool simplified,
+    AllocatorPtr alloc) const {
+  int64_t norm_count = x_shape.SizeToDimension(onnxruntime::narrow<size_t>(axis));
+  int64_t norm_size = x_shape.SizeFromDimension(onnxruntime::narrow<size_t>(axis));
+
+  const auto scale_size = scale_shape.Size();
+  const auto bias_size = (bias_data) ? bias_shape.Size() : 0;
+  if (scale_size != norm_size || (bias_data && bias_size != norm_size)) {
+    return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                           "Size of X.shape()[axis:] == ", norm_size,
+                           ". Size of scale and bias (if provided) must match this. Got scale size of ",
+                           scale_size, " and bias size of ", bias_size);
+  }
+
+  concurrency::ThreadPool::TryBatchParallelFor(
+      thread_pool, static_cast<int32_t>(norm_count),
+      [&](ptrdiff_t task_idx) {
+        ComputeJob(X_data, scale_data, bias_data, task_idx, norm_size, scale_fp32_, bias_fp32_,
+                   epsilon, simplified, Y_data, mean_data, inv_std_dev_data, alloc);
+      },
+      0);
+
+  return Status::OK();
 }
 
 }  // namespace onnxruntime
diff --git a/onnxruntime/core/providers/cpu/nn/layer_norm_impl.h b/onnxruntime/core/providers/cpu/nn/layer_norm_impl.h
index 393c637dbda18..f6325c31cc71a 100644
--- a/onnxruntime/core/providers/cpu/nn/layer_norm_impl.h
+++ b/onnxruntime/core/providers/cpu/nn/layer_norm_impl.h
@@ -4,6 +4,7 @@
 #pragma once
 
 #include "core/common/common.h"
+#include "core/framework/allocator.h"
 #include "core/framework/op_kernel.h"
 #include "core/framework/tensor.h"
 
@@ -14,11 +15,56 @@ class LayerNormImpl : public OpKernel {
   LayerNormImpl(const OpKernelInfo& op_kernel_info, bool simplified = false, bool contrib_op = false);
   Status Compute(OpKernelContext* p_op_kernel_context) const override;
 
+  Status PrePack(const Tensor& tensor, int input_idx, AllocatorPtr alloc,
+                 bool& is_packed, PrePackedWeights* prepacked_weights) override;
+
+  // This method was created so that it can be called directly from `test/onnx/microbenchmark/layer_normalization.cc`.
+  template <typename T, typename U>
+  Status ComputeWithoutContext(
+      const T* X_data,
+      const TensorShape& x_shape,
+      const T* scale_data,
+      const TensorShape& scale_shape,
+      const T* bias_data,
+      const TensorShape& bias_shape,
+      T* Y_data,
+      U* mean_data,
+      U* inv_std_dev,
+      onnxruntime::concurrency::ThreadPool* thread_pool,
+      int64_t axis,
+      float epsilon,
+      bool simplified,
+      AllocatorPtr alloc) const;
+
  private:
+  template <typename T, typename U>
+  Status ComputeImpl(OpKernelContext* p_ctx, int64_t orig_axis, float epsilon, bool simplified) const;
+
+  template <typename T>
+  struct SrcDispatcher {
+    Status operator()(const LayerNormImpl* p_instance, OpKernelContext* p_ctx, int64_t orig_axis,
+                      float epsilon, bool simplified, bool contrib_op) const {
+      // the contrib op kernel was always registered with the same type for all constraints.
+      // our implementation of the onnx op only supports 'float' as the U constraint.
+#if !defined(DISABLE_CONTRIB_OPS)
+      if (contrib_op) {
+        return p_instance->ComputeImpl<T, T>(p_ctx, orig_axis, epsilon, simplified);
+      } else
+#else
+      ORT_UNUSED_PARAMETER(contrib_op);
+#endif
+      {
+        return p_instance->ComputeImpl<T, float>(p_ctx, orig_axis, epsilon, simplified);
+      }
+    }
+  };
+
   int64_t axis_;
   float epsilon_;
   const bool simplified_;
   const bool contrib_op_;
+  mutable IAllocatorUniquePtr<float> scale_fp32_;
+  mutable IAllocatorUniquePtr<float> bias_fp32_;
 };
 
 }  // namespace onnxruntime
diff --git a/onnxruntime/test/onnx/microbenchmark/layer_normalization.cc b/onnxruntime/test/onnx/microbenchmark/layer_normalization.cc
new file mode 100644
index 0000000000000..75ce7b77acd4e
--- /dev/null
+++ b/onnxruntime/test/onnx/microbenchmark/layer_normalization.cc
@@ -0,0 +1,141 @@
+#ifdef _WIN32
+
+#include "core/platform/threadpool.h"
+#include "core/util/thread_utils.h"
+#include <benchmark/benchmark.h>
+
+#if defined(__GNUC__) && !defined(__clang__)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#endif
+
+#include "core/framework/allocator.h"
+#include "core/framework/config_options.h"
+#include "core/framework/data_transfer_manager.h"
+#include "core/framework/op_kernel_info.h"
+#include "core/framework/ort_value_name_idx_map.h"
+#include "core/platform/windows/env.h"
+#include "core/providers/cpu/nn/layer_norm_impl.h"
+#include "core/providers/cpu/cpu_provider_factory.h"
+#include "core/providers/cpu/cpu_provider_factory_creator.h"
+#include "core/util/thread_utils.h"
+
+#include "test/onnx/microbenchmark/common.h"
+
+#if defined(__GNUC__) && !defined(__clang__)
+#pragma GCC diagnostic pop
+#endif
+
+using namespace onnxruntime;
+
+namespace {
+
+std::vector<MLFloat16> createMLFloat16Vector(float* vals, int64_t num_elems) {
+  std::vector<MLFloat16> fp16vec;
+  fp16vec.reserve(num_elems);
+
+  for (int64_t i = 0; i < num_elems; i++) {
+    fp16vec.push_back(MLFloat16(vals[i]));
+  }
+
+  return fp16vec;
+}
+
+}  // namespace
+
+template <typename T, typename U>
+static void BM_LayerNormalization(benchmark::State& state) {
+  bool simplified = false;
+  const float epsilon = 1e-05f;
+  int64_t axis = 1;
+
+  onnxruntime::Node node;
+  // Required by LayerNormImpl constructor
+  node.AddAttribute("axis", axis);
+  node.AddAttribute("epsilon", epsilon);
+
+  KernelDef kernel_def;
+  std::unique_ptr<IExecutionProvider> execution_provider = CPUProviderFactoryCreator::Create(true)->CreateProvider();
+  std::unordered_map<int, OrtValue> constant_initialized_tensors;
+  OrtValueNameIdxMap mlvalue_name_idx_map;
+  DataTransferManager data_transfer_mgr;
+  AllocatorMap allocators;
+  ConfigOptions config_options;
+
+  OpKernelInfo op_kernel_info(node, kernel_def, *execution_provider, constant_initialized_tensors, mlvalue_name_idx_map,
+                              data_transfer_mgr, allocators, config_options);
+
+  LayerNormImpl layer_norm_impl(op_kernel_info);
+
+  const std::vector<int64_t> dims{1, 256, 1024};
+  const size_t num_elems = dims[0] * dims[1] * dims[2];
+
+  TensorShape x_shape(dims);
+  TensorShape scale_shape(dims);
+  TensorShape bias_shape(dims);
+
+  const float low = -1.0f;
+  const float high = 1.0f;
+
+  float* x_float = GenerateArrayWithRandomValue<float>(num_elems, low, high);
+  float* scale_float = GenerateArrayWithRandomValue<float>(num_elems, 0.1f, high);
+  float* bias_float = GenerateArrayWithRandomValue<float>(num_elems, low, high);
+
+  std::vector<MLFloat16> x_MLFloat16 = createMLFloat16Vector(x_float, num_elems);
+  std::vector<MLFloat16> scale_MLFloat16 = createMLFloat16Vector(scale_float, num_elems);
+  std::vector<MLFloat16> bias_MLFloat16 = createMLFloat16Vector(bias_float, num_elems);
+
+  T* x_data = nullptr;
+  T* scale_data = nullptr;
+  T* bias_data = nullptr;
+  if (std::is_same_v<T*, MLFloat16*>) {
+    x_data = (T*)x_MLFloat16.data();
+    scale_data = (T*)scale_MLFloat16.data();
+    bias_data = (T*)bias_MLFloat16.data();
+  } else if (std::is_same_v<T*, float*>) {
+    x_data = (T*)x_float;
+    scale_data = (T*)scale_float;
+    bias_data = (T*)bias_float;
+  }
+  assert(x_data);
+
+  T* Y_data = static_cast<T*>(aligned_alloc(num_elems * sizeof(T), 64));
+  U* mean_data = static_cast<U*>(aligned_alloc(num_elems * sizeof(U), 64));
+  U* inv_std_dev_data = static_cast<U*>(aligned_alloc(num_elems * sizeof(U), 64));
+
+  OrtThreadPoolParams tp_params;
+  tp_params.name = ORT_TSTR("intra-op");
+  std::unique_ptr<concurrency::ThreadPool> thread_pool = concurrency::CreateThreadPool(
+      &Env::Default(), tp_params, concurrency::ThreadPoolType::INTRA_OP);
+
+  OrtMemoryInfo memory_info(onnxruntime::CPU, OrtAllocatorType::OrtArenaAllocator);
+  AllocatorPtr alloc = std::make_shared<CPUAllocator>(memory_info);
+  for (auto _ : state) {
+    auto status = layer_norm_impl.ComputeWithoutContext(x_data, x_shape, scale_data, scale_shape, bias_data, bias_shape,
+                                                        Y_data, mean_data, inv_std_dev_data, thread_pool.get(), axis,
+                                                        epsilon, simplified, alloc);
+    if (!status.IsOK()) {
+      std::cout << "ComputeWithoutContext status not OK: " << status.ErrorMessage() << std::endl;
+      break;
+    }
+  }
+
+  aligned_free(x_float);
+  aligned_free(scale_float);
+  aligned_free(bias_float);
+  aligned_free(Y_data);
+  aligned_free(mean_data);
+  aligned_free(inv_std_dev_data);
+}
+
+BENCHMARK(BM_LayerNormalization<float, float>)
+    ->Arg(1)
+    ->UseRealTime()
+    ->Unit(benchmark::TimeUnit::kMicrosecond);
+
+BENCHMARK(BM_LayerNormalization<MLFloat16, float>)
+    ->Arg(1)
+    ->UseRealTime()
+    ->Unit(benchmark::TimeUnit::kMicrosecond);
+
+#endif