diff --git a/tensorflow/core/kernels/batch_matmul_op_impl.h b/tensorflow/core/kernels/batch_matmul_op_impl.h
index 5649c068780..d5b6e7d5e17 100644
--- a/tensorflow/core/kernels/batch_matmul_op_impl.h
+++ b/tensorflow/core/kernels/batch_matmul_op_impl.h
@@ -330,8 +330,26 @@ struct LaunchBatchMatMul<GPUDevice, Scalar> {
     auto* a_base_ptr = in_x.template flat<Scalar>().data();
     auto* b_base_ptr = in_y.template flat<Scalar>().data();
     auto* c_base_ptr = out->template flat<Scalar>().data();
+    uint64 a_stride;
+    uint64 b_stride;
+    uint64 c_stride;
 
-    if (!bcast.IsBroadcastingRequired()) {
+    bool is_full_broadcast =
+        std::min(bcast.x_batch_size(), bcast.y_batch_size()) == 1;
+    bool use_strided_batched =
+        (!bcast.IsBroadcastingRequired() || is_full_broadcast) &&
+        batch_size > 1;
+    if (use_strided_batched) {
+      a_stride = bcast.x_batch_size() != 1 ? m * k : 0;
+      b_stride = bcast.y_batch_size() != 1 ? k * n : 0;
+      c_stride = m * n;
+      a_device_memory.push_back(AsDeviceMemory(a_base_ptr));
+      b_device_memory.push_back(AsDeviceMemory(b_base_ptr));
+      c_device_memory.push_back(AsDeviceMemory(c_base_ptr));
+      a_ptrs.push_back(&a_device_memory.back());
+      b_ptrs.push_back(&b_device_memory.back());
+      c_ptrs.push_back(&c_device_memory.back());
+    } else if (!bcast.IsBroadcastingRequired()) {
       for (int64 i = 0; i < batch_size; ++i) {
         a_device_memory.push_back(AsDeviceMemory(a_base_ptr + i * m * k));
         b_device_memory.push_back(AsDeviceMemory(b_base_ptr + i * k * n));
@@ -407,6 +425,23 @@ struct LaunchBatchMatMul<GPUDevice, Scalar> {
               ", k=", k));
         }
       }
+    } else if (use_strided_batched) {
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemmStridedBatched(
+                  blas_transpose_b, blas_transpose_a, n, m, k,
+                  static_cast<Coefficient>(1.0), *b_ptrs[0], adj_y ? k : n,
+                  b_stride, *a_ptrs[0], adj_x ? m : k, a_stride,
+                  static_cast<Coefficient>(0.0), c_ptrs[0], n, c_stride,
+                  batch_size)
+              .ok();
+      if (!blas_launch_status) {
+        context->SetStatus(errors::Internal(
+            "Blas xGEMMStridedBatched launch failed : a.shape=",
+            in_x.shape().DebugString(),
+            ", b.shape=", in_y.shape().DebugString(), ", m=", m, ", n=", n,
+            ", k=", k, ", batch_size=", batch_size));
+      }
     } else {
       BlasScratchAllocator scratch_allocator(context);
       bool blas_launch_status =
@@ -467,7 +502,26 @@ struct LaunchBatchMatMul<GPUDevice, Eigen::half> {
     auto* b_base_ptr = in_y.template flat<Scalar>().data();
     auto* c_base_ptr = out->template flat<Scalar>().data();
 
-    if (!bcast.IsBroadcastingRequired()) {
+    uint64 a_stride;
+    uint64 b_stride;
+    uint64 c_stride;
+
+    bool is_full_broadcast =
+        std::min(bcast.x_batch_size(), bcast.y_batch_size()) == 1;
+    bool use_strided_batched =
+        (!bcast.IsBroadcastingRequired() || is_full_broadcast) &&
+        batch_size > 1;
+    if (use_strided_batched) {
+      a_stride = bcast.x_batch_size() != 1 ? m * k : 0;
+      b_stride = bcast.y_batch_size() != 1 ? k * n : 0;
+      c_stride = m * n;
+      a_device_memory.push_back(AsDeviceMemory(a_base_ptr));
+      b_device_memory.push_back(AsDeviceMemory(b_base_ptr));
+      c_device_memory.push_back(AsDeviceMemory(c_base_ptr));
+      a_ptrs.push_back(&a_device_memory.back());
+      b_ptrs.push_back(&b_device_memory.back());
+      c_ptrs.push_back(&c_device_memory.back());
+    } else if (!bcast.IsBroadcastingRequired()) {
       for (int64 i = 0; i < batch_size; ++i) {
         a_device_memory.push_back(AsDeviceMemory(a_base_ptr + i * m * k));
         b_device_memory.push_back(AsDeviceMemory(b_base_ptr + i * k * n));
@@ -518,6 +572,23 @@ struct LaunchBatchMatMul<GPUDevice, Eigen::half> {
             ", b.shape=", in_y.shape().DebugString(), ", m=", m, ", n=", n,
             ", k=", k));
       }
+    } else if (use_strided_batched) {
+      bool blas_launch_status =
+          stream
+              ->ThenBlasGemmStridedBatched(
+                  blas_transpose_b, blas_transpose_a, n, m, k,
+                  static_cast<Coefficient>(1.0), *b_ptrs[0], adj_y ? k : n,
+                  b_stride, *a_ptrs[0], adj_x ? m : k, a_stride,
+                  static_cast<Coefficient>(0.0), c_ptrs[0], n, c_stride,
+                  batch_size)
+              .ok();
+      if (!blas_launch_status) {
+        context->SetStatus(errors::Internal(
+            "Blas xGEMMStridedBatched launch failed : a.shape=",
+            in_x.shape().DebugString(),
+            ", b.shape=", in_y.shape().DebugString(), ", m=", m, ", n=", n,
+            ", k=", k, ", batch_size=", batch_size));
+      }
     } else {
       BlasScratchAllocator scratch_allocator(context);
       bool blas_launch_status =