diff --git a/paddle/fluid/operators/math/detail/gru_gpu_kernel.h b/paddle/fluid/operators/math/detail/gru_gpu_kernel.h
index 6b57da1046a05b15b9c3302104d9f4d12c52227f..77d7ff57cda7416705bed7eb393366e1f87232a0 100644
--- a/paddle/fluid/operators/math/detail/gru_gpu_kernel.h
+++ b/paddle/fluid/operators/math/detail/gru_gpu_kernel.h
@@ -101,6 +101,122 @@ __global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output,
   output_value[frame_idx] = r_output;
 }
 
+/*
+ * threads(tile_size, 1)
+ * grid(frame_blocks, 1)
+ */
+template <class T, int Tiled_size>
+__global__ void KeFastCollectiveGruGate(T *gate_value, T *prev_output_value,
+                                        T *gate_weight, T *reset_output,
+                                        int frame_size,
+                                        ActivationType active_node) {
+  T xt_0 = 0.0f;
+  T a0 = 0.0f;
+  T c0 = 0.0f;
+  T b0[Tiled_size];
+
+  int COL = blockIdx.x * blockDim.x + threadIdx.x;
+  int Tiled_mask = ((1 << Tiled_size) - 1);
+  // Tiled  matrix multiply using register shift, faster than sm.
+  if (prev_output_value) {
+    for (int k = 0; k < (((frame_size - 1) / Tiled_size) + 1); ++k) {
+      a0 = 0;
+      if ((threadIdx.x + k * Tiled_size) < frame_size) {
+        a0 = prev_output_value[threadIdx.x + (k * Tiled_size)];
+      }
+      for (int i = 0; i < Tiled_size; i++) {
+        if (COL < frame_size * 2 && (i + k * Tiled_size) < frame_size) {
+          b0[i] = gate_weight[(i + k * Tiled_size) * frame_size * 2 + COL];
+        }
+      }
+
+      for (int i = 0; i < Tiled_size; ++i) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
+        c0 = c0 + __shfl_sync(Tiled_mask, a0, i, Tiled_size) * b0[i];
+#else
+        c0 = c0 + __shfl(a0, i, Tiled_size) * b0[i];
+#endif
+      }
+    }
+  }
+
+  __syncthreads();
+
+  if (COL < frame_size * 2) {
+    xt_0 = gate_value[COL];
+    c0 += xt_0;
+    c0 = forward::activation(c0, active_node);
+    gate_value[COL] = c0;
+    if (frame_size <= COL && COL < frame_size * 2) {
+      T htp_0 = 0.0;
+      if (prev_output_value) {
+        htp_0 = prev_output_value[COL - frame_size];
+      }
+      reset_output[COL - frame_size] = c0 * htp_0;
+    } else if (COL < frame_size) {
+      gate_value[COL] = c0;
+    }
+  }
+}
+
+/*
+ * threads(tile_size, 1)
+ * grid(frame_blocks, 1)
+ */
+template <class T, int Tiled_size>
+__global__ void KeFastCollectiveGruOut(T *gate_weight, T *prev_out_value,
+                                       T *output_value, T *gate_value,
+                                       T *reset_value, int frame_size,
+                                       ActivationType act_node,
+                                       bool origin_mode) {
+  int COL = blockIdx.x * blockDim.x + threadIdx.x;
+
+  T a0 = 0.0f;
+  T b0[Tiled_size];
+  T c0 = 0.0f;
+
+  int Tiled_mask = ((1 << Tiled_size) - 1);
+  //- Tiled  matrix multiply with register shift
+  if (prev_out_value) {
+    for (int k = 0; k < (((frame_size - 1) / Tiled_size) + 1); ++k) {
+      a0 = 0;
+      if ((threadIdx.x + k * Tiled_size) < frame_size) {
+        a0 = reset_value[threadIdx.x + (k * Tiled_size)];
+      }
+      for (int i = 0; i < Tiled_size; i++) {
+        if (COL < frame_size && (i + k * Tiled_size) < frame_size) {
+          b0[i] = gate_weight[(i + k * Tiled_size) * frame_size + COL];
+        }
+      }
+
+      for (int i = 0; i < Tiled_size; ++i) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
+        c0 = c0 + __shfl_sync(Tiled_mask, a0, i, Tiled_size) * b0[i];
+#else
+        c0 = c0 + __shfl(a0, i, Tiled_size) * b0[i];
+#endif
+      }
+    }
+  }
+
+  __syncthreads();
+
+  if (COL < frame_size) {
+    T xt_0 = gate_value[COL + 2 * frame_size];
+    T gta_0 = gate_value[COL];
+    T htp_0 = 0;
+    if (prev_out_value) htp_0 = prev_out_value[COL];
+    c0 += xt_0;
+    c0 = forward::activation(c0, act_node);
+    gate_value[COL + 2 * frame_size] = c0;
+    if (origin_mode) {
+      output_value[COL] = htp_0 * gta_0 + (1 - gta_0) * c0;
+    } else {
+      output_value[COL] = c0 * gta_0 + (1 - gta_0) * htp_0;
+    }
+  }
+}
+
 /*
  * threads(frame_per_block, batch_per_block)
  * grid(frame_blocks, batch_blocks)
diff --git a/paddle/fluid/operators/math/gru_compute.cu b/paddle/fluid/operators/math/gru_compute.cu
index ec7e4d2228c38161bb1f3f97ec21b91db454adb4..75417cced237c48dda1f6e87c0647b10a66d0907 100644
--- a/paddle/fluid/operators/math/gru_compute.cu
+++ b/paddle/fluid/operators/math/gru_compute.cu
@@ -30,10 +30,25 @@ struct GRUUnitFunctor<platform::CUDADeviceContext, T> {
     dim3 threads;
     dim3 grid;
     if (batch_size == 1) {
-      int frame_per_block = frame_size <= 1024 ? frame_size : 1024;
-      int frame_blocks = (frame_size + 1024 - 1) / 1024;
-      threads = dim3(frame_per_block, 1);
+      constexpr int tiled_size = 16;
+      int frame_blocks = (frame_size * 2 + tiled_size - 1) / tiled_size;
+      threads = dim3(tiled_size, 1);
       grid = dim3(frame_blocks, 1);
+
+      detail::KeFastCollectiveGruGate<T,
+                                      tiled_size><<<grid, threads, 0, stream>>>(
+          value.gate_value, value.prev_out_value, value.gate_weight,
+          value.reset_output_value, frame_size, active_gate);
+
+      frame_blocks = (frame_size + tiled_size - 1) / tiled_size;
+      grid = dim3(frame_blocks, 1);
+      detail::KeFastCollectiveGruOut<T,
+                                     tiled_size><<<grid, threads, 0, stream>>>(
+          value.state_weight, value.prev_out_value, value.output_value,
+          value.gate_value, value.reset_output_value, frame_size, active_node,
+          origin_mode);
+
+      return;
     } else {
       threads = dim3(32, 32);
       grid = dim3((frame_size + 32 - 1) / 32, (batch_size + 32 - 1) / 32);