diff --git a/onnxruntime/core/providers/vsinpu/patches/mlas_crosscompiling.patch b/onnxruntime/core/providers/vsinpu/patches/mlas_crosscompiling.patch
index b089818f82..45de47f3e5 100644
--- a/onnxruntime/core/providers/vsinpu/patches/mlas_crosscompiling.patch
+++ b/onnxruntime/core/providers/vsinpu/patches/mlas_crosscompiling.patch
@@ -1,5 +1,5 @@
 diff --git a/cmake/onnxruntime_mlas.cmake b/cmake/onnxruntime_mlas.cmake
-index 66f4aea606..481109e560 100644
+index c02ac2096d..2bc51298f0 100644
 --- a/cmake/onnxruntime_mlas.cmake
 +++ b/cmake/onnxruntime_mlas.cmake
 @@ -361,7 +361,7 @@ else()
@@ -12,7 +12,7 @@ index 66f4aea606..481109e560 100644
              ${mlas_platform_srcs}
              ${MLAS_SRC_DIR}/aarch64/HalfGemmKernelNeon.S
 diff --git a/onnxruntime/core/mlas/inc/mlas.h b/onnxruntime/core/mlas/inc/mlas.h
-index 675f7c7a13..eb7ed77911 100644
+index e46105324a..414c46a1ce 100644
 --- a/onnxruntime/core/mlas/inc/mlas.h
 +++ b/onnxruntime/core/mlas/inc/mlas.h
 @@ -82,6 +82,9 @@ Abstract:
@@ -33,3 +33,217 @@ index 675f7c7a13..eb7ed77911 100644
  #endif //
  #endif // ARM64
  #endif // Visual Studio 16 or earlier does not support fp16 intrinsic
+@@ -1635,6 +1639,7 @@ MlasHalfGemmConvertPackB(
+     );
+ 
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+ /**
+  * @brief Whether current CPU supports Bfloat16(bf16) acceleration.
+  */
+@@ -1746,6 +1751,7 @@ MlasSBGemmPackBSize(size_t N, size_t K);
+ void MLASCALL
+ MlasSBGemmConvertPackB(size_t N, size_t K, const float* B, size_t ldb, void* PackedB);
+ #endif
++#endif
+ 
+ /**
+  * @brief Indirect Depthwise convolution for fp16
+diff --git a/onnxruntime/core/mlas/lib/mlasi.h b/onnxruntime/core/mlas/lib/mlasi.h
+index 4239e2ecae..3df7e5573d 100644
+--- a/onnxruntime/core/mlas/lib/mlasi.h
++++ b/onnxruntime/core/mlas/lib/mlasi.h
+@@ -361,6 +361,7 @@ size_t
+ #else
+ 
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+ typedef size_t(MLASCALL MLAS_SBGEMM_FLOAT_KERNEL)(
+     const float* A,
+     const bfloat16_t* B,
+@@ -373,6 +374,7 @@ typedef size_t(MLASCALL MLAS_SBGEMM_FLOAT_KERNEL)(
+     const float* Bias
+ );
+ #endif
++#endif
+ 
+ typedef
+ size_t
+@@ -763,8 +765,10 @@ extern "C" {
+     MLAS_GEMM_FLOAT_KERNEL MlasSgemmKernelZero;
+     MLAS_GEMM_FLOAT_KERNEL MlasSgemmKernelAdd;
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+     MLAS_SBGEMM_FLOAT_KERNEL MlasSbgemmKernelZero;
+     MLAS_SBGEMM_FLOAT_KERNEL MlasSbgemmKernelAdd;
++#endif
+ #endif
+     MLAS_GEMM_DOUBLE_KERNEL MlasDgemmKernelZero;
+     MLAS_GEMM_DOUBLE_KERNEL MlasDgemmKernelAdd;
+@@ -899,8 +903,10 @@ extern "C" {
+ #define MLAS_QGEMM_THREAD_COMPLEXITY                65536
+ 
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+ #define MLAS_SBGEMM_THREAD_COMPLEXITY (size_t(64) * size_t(1024))
+ #endif
++#endif
+ 
+ //
+ // Single-threaded single precision matrix/matrix multiply operation.
+@@ -2570,4 +2576,3 @@ MlasPackInt4Elements(uint8_t* Output, UnpackedType ValueLow, UnpackedType ValueH
+     static_assert(std::is_same_v<UnpackedType, uint8_t> || std::is_same_v<UnpackedType, int8_t>);
+     *Output = static_cast<uint8_t>(((ValueHigh & 0xF) << 4) | (ValueLow & 0xF));
+ }
+-
+diff --git a/onnxruntime/core/mlas/lib/platform.cpp b/onnxruntime/core/mlas/lib/platform.cpp
+index ed437f20f7..8c9d0a75fd 100644
+--- a/onnxruntime/core/mlas/lib/platform.cpp
++++ b/onnxruntime/core/mlas/lib/platform.cpp
+@@ -20,7 +20,7 @@ Abstract:
+ #include <thread>
+ #include <mutex>
+ 
+-#if defined(MLAS_TARGET_POWER) 
++#if defined(MLAS_TARGET_POWER)
+ #if defined(__linux__)
+ #include <sys/auxv.h>
+ #elif defined(_AIX)
+@@ -536,7 +536,7 @@ Return Value:
+         this->SQNBitGemmDispatch = &MlasSQNBitGemmDispatchNeon;
+     }
+ 
+-#if defined(__linux__)
++#if defined(__linux__) && !defined(USE_VSINPU)
+     //
+     // Check if the processor supports ASIMD I8MM instructions.
+     //
+diff --git a/onnxruntime/core/mlas/lib/sbgemm.h b/onnxruntime/core/mlas/lib/sbgemm.h
+index de7fd72fad..4f75dbd6fa 100644
+--- a/onnxruntime/core/mlas/lib/sbgemm.h
++++ b/onnxruntime/core/mlas/lib/sbgemm.h
+@@ -31,6 +31,7 @@ Abstract:
+ --*/
+ 
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+ 
+ #pragma once
+ 
+@@ -396,4 +397,5 @@ MlasSBGemmBatch(const size_t M, const size_t N, const size_t K, const size_t Bat
+         }
+     );
+ }
++#endif
+ #endif  // defined(__aarch64__) && defined(__linux__)
+diff --git a/onnxruntime/core/providers/cpu/math/matmul.cc b/onnxruntime/core/providers/cpu/math/matmul.cc
+index 6a71283f9d..d8bd348854 100644
+--- a/onnxruntime/core/providers/cpu/math/matmul.cc
++++ b/onnxruntime/core/providers/cpu/math/matmul.cc
+@@ -132,7 +132,7 @@ Status MatMul<T>::Compute(OpKernelContext* ctx) const {
+ 
+   return Status::OK();
+ }
+-#if defined(__aarch64__) && defined(__linux__)
++#if defined(__aarch64__) && defined(__linux__) && !defined(USE_VSINPU)
+ bool GemmPackBBfloat16(AllocatorPtr& alloc,
+                        const Tensor& tensor_b,
+                        bool trans_b,
+@@ -180,6 +180,7 @@ Status MatMul<float>::PrePack(const Tensor& tensor, int input_idx, /*out*/ Alloc
+   if (input_idx == 1) {
+     size_t packed_b_size;
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+     size_t dim1 = 0;
+     size_t dim2 = 0;
+     TensorShape b_shape = tensor.Shape();
+@@ -192,6 +193,7 @@ Status MatMul<float>::PrePack(const Tensor& tensor, int input_idx, /*out*/ Alloc
+     if (use_fastmath_mode_ && (trans_b_attr_ == 0) && ((dim1 * dim2) >= kFastMathModeKernelsizeThreshold)) {
+       is_packed = GemmPackBBfloat16(alloc, tensor, trans_b_attr_ != 0, packed_b_, packed_b_size, b_shape_);
+     } else
++#endif
+ #endif
+     {
+       is_packed = GemmPackBFp32(alloc, tensor, trans_b_attr_ != 0, packed_b_, packed_b_size, b_shape_);
+@@ -257,6 +259,7 @@ Status MatMul<float>::Compute(OpKernelContext* ctx) const {
+   const size_t lda = helper.Lda(trans_a);
+   const size_t ldb = helper.Ldb(trans_b);
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+   if (use_fastmath_mode_ && !trans_b && ((N * K) >= kFastMathModeKernelsizeThreshold)) {
+     std::vector<MLAS_SBGEMM_DATA_PARAMS> data(max_len);
+     for (size_t i = 0; i < max_len; i++) {
+@@ -273,6 +276,7 @@ Status MatMul<float>::Compute(OpKernelContext* ctx) const {
+     }
+     MlasSBGemmBatch(M, N, K, max_len, data.data(), thread_pool);
+   } else
++#endif
+ #endif
+   {
+     std::vector<MLAS_SGEMM_DATA_PARAMS> data(max_len);
+diff --git a/onnxruntime/core/providers/cpu/math/matmul.h b/onnxruntime/core/providers/cpu/math/matmul.h
+index b9bbe36583..2f570502d2 100644
+--- a/onnxruntime/core/providers/cpu/math/matmul.h
++++ b/onnxruntime/core/providers/cpu/math/matmul.h
+@@ -31,8 +31,10 @@ class MatMul<float> final : public OpKernel {
+     trans_batch_b_ = trans_batch_b_attr != 0;
+ 
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+     auto config_ops = info.GetConfigOptions().GetConfigEntry(kOrtSessionOptionsMlasGemmFastMathArm64Bfloat16);
+     use_fastmath_mode_ = (config_ops == "1") && MlasBf16AccelerationSupported();
++#endif
+ #endif
+   }
+ 
+@@ -57,12 +59,14 @@ class MatMul<float> final : public OpKernel {
+   bool trans_batch_b_;
+ 
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+   // fastmath mode state
+   bool use_fastmath_mode_;
+   // sbgemm kernel is implemented as 8x8 blocks with weights pre-packed to 4 blocks of 4x2
+   // so a minimum of 32 elements is defined to outweigh the additional prepacking overhead
+   const size_t kFastMathModeKernelsizeThreshold = 32;
+ #endif
++#endif
+ };
+ 
+ }  // namespace onnxruntime
+diff --git a/onnxruntime/test/mlas/unittest/test_sbgemm.cpp b/onnxruntime/test/mlas/unittest/test_sbgemm.cpp
+index f85fe97776..6039b7fa9e 100644
+--- a/onnxruntime/test/mlas/unittest/test_sbgemm.cpp
++++ b/onnxruntime/test/mlas/unittest/test_sbgemm.cpp
+@@ -16,6 +16,7 @@ Abstract:
+ --*/
+ 
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+ 
+ #include "test_sbgemm.h"
+ 
+@@ -138,4 +139,5 @@ static UNUSED_VARIABLE bool added_to_main = AddTestRegister([](bool is_short_exe
+   }
+   return SBGemmRegistLongExecute() > 0;
+ });
++#endif
+ #endif  // defined(__aarch64__) && defined(__linux__)
+diff --git a/onnxruntime/test/mlas/unittest/test_sbgemm.h b/onnxruntime/test/mlas/unittest/test_sbgemm.h
+index 13701e2e3d..7e432f53c2 100644
+--- a/onnxruntime/test/mlas/unittest/test_sbgemm.h
++++ b/onnxruntime/test/mlas/unittest/test_sbgemm.h
+@@ -16,6 +16,7 @@ Abstract:
+ --*/
+ 
+ #if defined(__aarch64__) && defined(__linux__)
++#if !defined(USE_VSINPU)
+ 
+ #pragma once
+ 
+@@ -278,4 +279,5 @@ class MlasSBGemmTest : public MlasTestBase {
+   }
+ };
+ 
++#endif
+ #endif  // defined(__aarch64__) && defined(__linux__)