diff --git a/paddle/fluid/operators/jit/benchmark.cc b/paddle/fluid/operators/jit/benchmark.cc
index 65241b270a2175a869dc85a5075e854e3a399b12..8dab16c284a6f483dce2d8b1dc729391e2e76b8d 100644
--- a/paddle/fluid/operators/jit/benchmark.cc
+++ b/paddle/fluid/operators/jit/benchmark.cc
@@ -213,7 +213,7 @@ void BenchSeqPoolKernel() {
 template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
 void BenchMatMulKernel() {
   for (int m : {1, 2, 3, 4}) {
-    for (int n : {1, 2, 3, 4}) {
+    for (int n : TestSizes()) {
       for (int k : TestSizes()) {
         std::vector<T> a(m * k), b(k * n), c(m * n);
         RandomVec<T>(m * k, a.data(), -2.f, 2.f);
diff --git a/paddle/fluid/operators/jit/more/mkl/CMakeLists.txt b/paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
index f5fd1b3d240951b73a7c87a39f2343a4473fcf31..7c6a75d35f654b6c2c46b4d498e401db92237341 100644
--- a/paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
+++ b/paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
@@ -3,7 +3,7 @@ cc_library(jit_kernel_mkl SRCS mkl.cc DEPS jit_kernel_base dynload_mklml)
 set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} dynload_mklml jit_kernel_mkl PARENT_SCOPE)
 
 # use mkl kernels by name and type
-# USE_JITKERNEL_MORE(kMatMul, mkl)
+USE_JITKERNEL_MORE(kMatMul, mkl)
 USE_JITKERNEL_MORE(kVMul, mkl)
 USE_JITKERNEL_MORE(kVAdd, mkl)
 USE_JITKERNEL_MORE(kVScal, mkl)
diff --git a/paddle/fluid/operators/jit/more/mkl/mkl.cc b/paddle/fluid/operators/jit/more/mkl/mkl.cc
index 5a499ac2c02aa70d2824f0d3be618e083ba10334..5b20ae4da96f262657e487cd231b5f319bf92641 100644
--- a/paddle/fluid/operators/jit/more/mkl/mkl.cc
+++ b/paddle/fluid/operators/jit/more/mkl/mkl.cc
@@ -24,6 +24,20 @@ namespace jit {
 namespace more {
 namespace mkl {
 
+template <>
+void MatMul<float>(const float* a, const float* b, float* c, int m, int n,
+                   int k) {
+  platform::dynload::cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, m,
+                                 n, k, 1.f, a, k, b, n, 0.f, c, n);
+}
+
+template <>
+void MatMul<double>(const double* a, const double* b, double* c, int m, int n,
+                    int k) {
+  platform::dynload::cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, m,
+                                 n, k, 1.0, a, k, b, n, 0.0, c, n);
+}
+
 template <>
 void VMul<float>(const float* x, const float* y, float* z, int n) {
   platform::dynload::vsMul(n, x, y, z);
@@ -93,6 +107,11 @@ void VAXPY<double>(double a, const double* x, double* y, int n) {
 }
 
 // TODO(TJ): tuning me carefully on AVX, AVX2 and AVX512
+template <>
+bool MatMulKernel<float>::UseMe(const int& d) const {
+  return platform::MayIUse(platform::avx);
+}
+
 template <>
 bool VMulKernel<float>::UseMe(const int& d) const {
   return platform::MayIUse(platform::avx512f) && d > 512;
@@ -139,6 +158,7 @@ bool SeqPoolKernel<double>::UseMe(const seq_pool_attr_t& attr) const {
     return true;                                         \
   }
 
+AWALYS_USE_ME_WITH_DOUBLE(MatMul);
 AWALYS_USE_ME_WITH_DOUBLE(VMul);
 AWALYS_USE_ME_WITH_DOUBLE(VAdd);
 AWALYS_USE_ME_WITH_DOUBLE(VScal);
@@ -159,6 +179,7 @@ namespace mkl = paddle::operators::jit::more::mkl;
   REGISTER_JITKERNEL_MORE(key, mkl, mkl::func##Kernel<float>, \
                           mkl::func##Kernel<double>)
 
+REGISTER_MKL_KERNEL(kMatMul, MatMul);
 REGISTER_MKL_KERNEL(kVMul, VMul);
 REGISTER_MKL_KERNEL(kVAdd, VAdd);
 REGISTER_MKL_KERNEL(kVScal, VScal);
diff --git a/paddle/fluid/operators/jit/more/mkl/mkl.h b/paddle/fluid/operators/jit/more/mkl/mkl.h
index 0a3816db24ccd0820cb259b40044e1f5b66665f7..314ef73d8a581eaaf16ff3b8a58189d209b1735f 100644
--- a/paddle/fluid/operators/jit/more/mkl/mkl.h
+++ b/paddle/fluid/operators/jit/more/mkl/mkl.h
@@ -24,6 +24,9 @@ namespace jit {
 namespace more {
 namespace mkl {
 
+template <typename T>
+void MatMul(const T* a, const T* b, T* c, int m, int n, int k);
+
 template <typename T>
 void VMul(const T* x, const T* y, T* z, int n);
 
@@ -93,6 +96,9 @@ void SeqPool(const T* x, T* y, const seq_pool_attr_t* attr) {
     const char* ImplType() const override { return "MKL"; }          \
   }
 
+// ABCMNK
+DECLARE_MKL_KERNEL(MatMul, MatMulTuples);
+
 // XYZN
 DECLARE_MKL_KERNEL(VMul, XYZNTuples);
 DECLARE_MKL_KERNEL(VAdd, XYZNTuples);
diff --git a/paddle/fluid/operators/jit/refer/refer.h b/paddle/fluid/operators/jit/refer/refer.h
index cbf799cbd63157f5ec7bdca452d832ea32ff95ed..225319c059e581dd83049c5bf9b93c4ed516de0a 100644
--- a/paddle/fluid/operators/jit/refer/refer.h
+++ b/paddle/fluid/operators/jit/refer/refer.h
@@ -356,7 +356,20 @@ void SeqPool(const T* x, T* y, const seq_pool_attr_t* attr) {
 
 // A(M,K) * B(K,N) = C(M,N)
 template <typename T>
-void MatMul(const T* A, const T* B, T* C, int M, int N, int K) {}
+void MatMul(const T* A, const T* B, T* C, int M, int N, int K) {
+  for (int m = 0; m < M; ++m) {
+    const T* pa = A + m * K;
+    T* pc = C + m * N;
+    for (int n = 0; n < N; ++n) {
+      const T* pb = B + n;
+      T sum = static_cast<T>(0);
+      for (int k = 0; k < K; ++k) {
+        sum += (pa[k] * pb[k * N]);
+      }
+      *(pc + n) = sum;
+    }
+  }
+}
 
 #define DECLARE_REFER_KERNEL(name, tuples)             \
   template <typename T>                                \
diff --git a/paddle/fluid/operators/jit/test.cc b/paddle/fluid/operators/jit/test.cc
index e6a9690a47421be4dbc2832bb7267c980e85d0f2..1246ee7c24e0de1363fb35351d15fd4bb912c9e8 100644
--- a/paddle/fluid/operators/jit/test.cc
+++ b/paddle/fluid/operators/jit/test.cc
@@ -230,7 +230,8 @@ struct TestFuncWithRefer<jit::SeqPoolTuples<T>, std::vector<T>,
 };
 
 template <typename T>
-struct TestFuncWithRefer<jit::MatMulTuples<T>, std::vector<T>, std::vector<T>> {
+struct TestFuncWithRefer<jit::MatMulTuples<T>, std::vector<T>, std::vector<T>,
+                         std::vector<T>, int, int, int> {
   void operator()(const typename jit::MatMulTuples<T>::func_type tgt,
                   const std::vector<T>& a, const std::vector<T>& b,
                   const std::vector<T>& cref, int m, int n, int k) {
@@ -486,8 +487,8 @@ void TestMatMulKernel() {
         auto ref = jit::GetRefer<KT, jit::MatMulTuples<T>>();
         EXPECT_TRUE(ref != nullptr);
         std::vector<T> a(m * k), b(k * n), c(m * n);
-        RandomVec<T>(m * k, a.data(), -2.f, 2.f);
-        RandomVec<T>(k * n, b.data(), -2.f, 2.f);
+        RandomVec<T>(m * k, a.data(), -0.2f, 0.2f);
+        RandomVec<T>(k * n, b.data(), -0.2f, 0.2f);
         const T* a_data = a.data();
         const T* b_data = b.data();
         T* c_data = c.data();