diff --git a/paddle/fluid/inference/api/paddle_analysis_config.h b/paddle/fluid/inference/api/paddle_analysis_config.h
index 067d8986b140ca373b65eb985bea4632f7a735cd..4d6d1ab36e45b9a857fd2610315b4ad65c00945f 100644
--- a/paddle/fluid/inference/api/paddle_analysis_config.h
+++ b/paddle/fluid/inference/api/paddle_analysis_config.h
@@ -526,20 +526,20 @@ struct AnalysisConfig {
   bool use_tensorrt_{false};
   // For workspace_size, refer it from here:
   // https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
-  int tensorrt_workspace_size_;
+  int tensorrt_workspace_size_{1 << 30};
   // While TensorRT allows an engine optimized for a given max batch size
   // to run at any smaller size, the performance for those smaller
   // sizes may not be as well-optimized. Therefore, Max batch is best
   // equivalent to the runtime batch size.
-  int tensorrt_max_batchsize_;
+  int tensorrt_max_batchsize_{1};
   //  We transform the Ops that can be converted into TRT layer in the model,
   //  and aggregate these Ops into subgraphs for TRT execution.
   //  We set this variable to control the minimum number of nodes in the
   //  subgraph, 3 as default value.
   int tensorrt_min_subgraph_size_{3};
-  Precision tensorrt_precision_mode_;
-  bool trt_use_static_engine_;
-  bool trt_use_calib_mode_;
+  Precision tensorrt_precision_mode_{Precision::kFloat32};
+  bool trt_use_static_engine_{false};
+  bool trt_use_calib_mode_{true};
   std::map<std::string, std::vector<int>> min_input_shape_{};
   std::map<std::string, std::vector<int>> max_input_shape_{};
   std::map<std::string, std::vector<int>> optim_input_shape_{};