diff --git a/paddle/fluid/inference/tensorrt/plugin/matmul_op_int8_plugin.cu b/paddle/fluid/inference/tensorrt/plugin/matmul_op_int8_plugin.cu
index 551b9c6c72f1f420542bfa2a64999afabe053894..88e075386d09358c9548828c13141f79873c3da0 100644
--- a/paddle/fluid/inference/tensorrt/plugin/matmul_op_int8_plugin.cu
+++ b/paddle/fluid/inference/tensorrt/plugin/matmul_op_int8_plugin.cu
@@ -299,13 +299,13 @@ void MatmulPlugin::configurePlugin(const nvinfer1::PluginTensorDesc* inputs,
         matmulDesc_, CUBLASLT_MATMUL_DESC_POINTER_MODE, &matmul_model,
         sizeof(matmul_model)));
 
-    float alpha_tem[n_];
+    std::vector<float> alpha_tem(n_, 0);
     for (int i = 0; i < n_; i++) {
       alpha_tem[i] = alpha_ * inscale_0 * inscale_1 / outscale;
     }
     PADDLE_ENFORCE_CUDA_SUCCESS(
         cudaMalloc((void**)&alpha_scale_, n_ * sizeof(float)));
-    cudaMemcpyAsync(alpha_scale_, alpha_tem, n_ * sizeof(float),
+    cudaMemcpyAsync(alpha_scale_, &alpha_tem[0], n_ * sizeof(float),
                     cudaMemcpyHostToDevice);
     float zero_tem = zero;
     PADDLE_ENFORCE_CUDA_SUCCESS(
@@ -624,13 +624,13 @@ void MatmulPluginDynamic::configurePlugin(
       sizeof(int8_t) * ((m_max + 32 - 1) / 32 * 32) / 32 * ldctransform));
 
   if (type_ == nvinfer1::DataType::kINT8) {
-    float alpha_tem[n_max];
+    std::vector<float> alpha_tem(n_max, 0);
     for (int i = 0; i < n_max; i++) {
       alpha_tem[i] = alpha_ * inscale_0 * inscale_1 / outscale;
     }
     PADDLE_ENFORCE_CUDA_SUCCESS(
         cudaMalloc((void**)&alpha_scale_, n_max * sizeof(float)));
-    cudaMemcpyAsync(alpha_scale_, alpha_tem, n_max * sizeof(float),
+    cudaMemcpyAsync(alpha_scale_, &alpha_tem[0], n_max * sizeof(float),
                     cudaMemcpyHostToDevice);
     float zero_tem = zero;
     PADDLE_ENFORCE_CUDA_SUCCESS(