diff --git a/mobile/src/io/api_paddle_mobile.cc b/mobile/src/io/api_paddle_mobile.cc
index 4c6acb2567a2e6a26048d006b2b2d6dce17ebd0c..8bfc91998f600726c1bcf8fe932372928928e334 100644
--- a/mobile/src/io/api_paddle_mobile.cc
+++ b/mobile/src/io/api_paddle_mobile.cc
@@ -41,6 +41,10 @@ bool PaddleMobilePredictor<Device, T>::Init(const PaddleMobileConfig &config) {
   if (config.pre_post_type == PaddleMobileConfig::UINT8_255) {
     configInternal.pre_post_type = PrePostType::UINT8_255;
   }
+
+  configInternal.memory_optimization_level =
+      config.mem_opt ? MemoryOptimizationWithoutFeeds : NoMemoryOptimization;
+
   paddle_mobile_.reset(new PaddleMobile<Device, T>(configInternal));
 #ifdef PADDLE_MOBILE_CL
   paddle_mobile_->SetCLPath(config.cl_path);
diff --git a/mobile/src/io/paddle_inference_api.h b/mobile/src/io/paddle_inference_api.h
index 001b2c9bf02d2484f5fb8839802531f863535f84..c89b998144badcf7b88dbbfcaa631a25df7892d5 100644
--- a/mobile/src/io/paddle_inference_api.h
+++ b/mobile/src/io/paddle_inference_api.h
@@ -221,6 +221,7 @@ struct PaddleMobileConfig : public PaddlePredictor::Config {
   bool lod_mode = false;
   int thread_num = 1;
   bool load_when_predict = false;
+  bool mem_opt = true;
   std::string cl_path;
   struct PaddleModelMemoryPack memory_pack;
 };
diff --git a/mobile/test/net/test_net_performance.cpp b/mobile/test/net/test_net_performance.cpp
index 678558d602e2926e6a1f6786bb95281828ff3677..95e72ea7a77d38f07abd391326120b136b4cc499 100644
--- a/mobile/test/net/test_net_performance.cpp
+++ b/mobile/test/net/test_net_performance.cpp
@@ -156,22 +156,41 @@ void test(int argc, char *argv[]) {
     }
 
     // 测速
-    auto time5 = time();
+    auto max_time = -1;
+    auto min_time = 100000;
+    auto all_time = 0;
     if (is_lod) {
       for (int i = 0; i < run_times; i++) {
+        auto time7 = time();
         paddle_mobile.Predict(input_lod_tensor);
+        auto time8 = time();
+        const double diff_time_single = time_diff(time7, time8);
+        max_time = fmax(diff_time_single, max_time);
+        min_time = fmin(diff_time_single, min_time);
+        all_time += diff_time_single;
       }
     } else {
       paddle_mobile.Feed(var_names[0], input_tensor);
       for (int i = 0; i < run_times; i++) {
+        auto time7 = time();
         paddle_mobile.Predict();
+        auto time8 = time();
+        const double diff_time_single = time_diff(time7, time8);
+        max_time = fmax(diff_time_single, max_time);
+        min_time = fmin(diff_time_single, min_time);
+        all_time += diff_time_single;
       }
     }
 
-    auto time6 = time();
     std::cout << "auto-test"
-              << " predict-time-cost " << time_diff(time5, time6) / run_times
+              << " predict-time-cost-avg " << all_time * 1.0f / run_times
               << "ms" << std::endl;
+    std::cout << "auto-test"
+              << " predict-time-cost-max " << double(max_time) << "ms"
+              << std::endl;
+    std::cout << "auto-test"
+              << " predict-time-cost-min " << double(min_time) << "ms"
+              << std::endl;
 
     std::cout << std::endl;
   }