diff --git a/examples/vision/ocr/PP-OCRv2/cpp/infer.cc b/examples/vision/ocr/PP-OCRv2/cpp/infer.cc
index 9d628689b..2537c12bb 100644
--- a/examples/vision/ocr/PP-OCRv2/cpp/infer.cc
+++ b/examples/vision/ocr/PP-OCRv2/cpp/infer.cc
@@ -29,9 +29,25 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
   auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
   auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
 
-  auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, option);
-  auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, option);
-  auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, option);
+  auto det_option = option;
+  auto cls_option = option;
+  auto rec_option = option;
+
+  // If use TRT backend, the dynamic shape will be set as follow.
+  det_option.SetTrtInputShape("x", {1, 3, 50, 50}, {1, 3, 640, 640},
+                                {1, 3, 1536, 1536});
+  cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320}, {1, 3, 48, 1024});
+  rec_option.SetTrtInputShape("x", {1, 3, 32, 10}, {1, 3, 32, 320},
+                                {1, 3, 32, 2304});
+  
+  // Users could save TRT cache file to disk as follow. 
+  // det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt");
+  // cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt");
+  // rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt");
+
+  auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, det_option);
+  auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
+  auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
 
   assert(det_model.Initialized());
   assert(cls_model.Initialized());
diff --git a/examples/vision/ocr/PP-OCRv2/python/infer.py b/examples/vision/ocr/PP-OCRv2/python/infer.py
index 984ede8e7..1bb94eb7e 100644
--- a/examples/vision/ocr/PP-OCRv2/python/infer.py
+++ b/examples/vision/ocr/PP-OCRv2/python/infer.py
@@ -96,19 +96,29 @@ rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
 rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
 rec_label_file = args.rec_label_file
 
-# 对于三个模型，均采用同样的部署配置
-# 用户也可根据自行需求分别配置
-runtime_option = build_option(args)
+det_option = runtime_option
+cls_option = runtime_option
+rec_option = runtime_option
+
+# 当使用TRT时，分别给三个Runtime设置动态shape
+det_option.set_trt_input_shape("x", [1, 3, 50, 50], [1, 3, 640, 640],
+                               [1, 3, 1536, 1536])
+cls_option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
+                               [1, 3, 48, 1024])
+rec_option.set_trt_input_shape("x", [1, 3, 32, 10], [1, 3, 32, 320],
+                               [1, 3, 32, 2304])
+
+# 用户可以把TRT引擎文件保存至本地
+# det_option.set_trt_cache_file(args.det_model  + "/det_trt_cache.trt")
+# cls_option.set_trt_cache_file(args.cls_model  + "/cls_trt_cache.trt")
+# rec_option.set_trt_cache_file(args.rec_model  + "/rec_trt_cache.trt")
 
 det_model = fd.vision.ocr.DBDetector(
-    det_model_file, det_params_file, runtime_option=runtime_option)
+    det_model_file, det_params_file, runtime_option=det_option)
 cls_model = fd.vision.ocr.Classifier(
-    cls_model_file, cls_params_file, runtime_option=runtime_option)
+    cls_model_file, cls_params_file, runtime_option=cls_option)
 rec_model = fd.vision.ocr.Recognizer(
-    rec_model_file,
-    rec_params_file,
-    rec_label_file,
-    runtime_option=runtime_option)
+    rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
 
 # 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
 ppocr_v2 = fd.vision.ocr.PPOCRv2(
diff --git a/examples/vision/ocr/PP-OCRv3/cpp/infer.cc b/examples/vision/ocr/PP-OCRv3/cpp/infer.cc
index 333dbaa3f..da950872b 100644
--- a/examples/vision/ocr/PP-OCRv3/cpp/infer.cc
+++ b/examples/vision/ocr/PP-OCRv3/cpp/infer.cc
@@ -29,9 +29,25 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
   auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
   auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
 
-  auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, option);
-  auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, option);
-  auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, option);
+  auto det_option = option;
+  auto cls_option = option;
+  auto rec_option = option;
+
+  // If use TRT backend, the dynamic shape will be set as follow. 
+  det_option.SetTrtInputShape("x", {1, 3, 50, 50}, {1, 3, 640, 640},
+                                {1, 3, 1536, 1536});
+  cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320}, {1, 3, 48, 1024});
+  rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320},
+                                {1, 3, 48, 2304});
+  
+  // Users could save TRT cache file to disk as follow. 
+  // det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt");
+  // cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt");
+  // rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt");
+
+  auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, det_option);
+  auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
+  auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
 
   assert(det_model.Initialized());
   assert(cls_model.Initialized());
diff --git a/examples/vision/ocr/PP-OCRv3/python/infer.py b/examples/vision/ocr/PP-OCRv3/python/infer.py
index 46df9c507..43b9b630c 100644
--- a/examples/vision/ocr/PP-OCRv3/python/infer.py
+++ b/examples/vision/ocr/PP-OCRv3/python/infer.py
@@ -100,15 +100,29 @@ rec_label_file = args.rec_label_file
 # 用户也可根据自行需求分别配置
 runtime_option = build_option(args)
 
+det_option = runtime_option
+cls_option = runtime_option
+rec_option = runtime_option
+
+# 当使用TRT时，分别给三个Runtime设置动态shape
+det_option.set_trt_input_shape("x", [1, 3, 50, 50], [1, 3, 640, 640],
+                               [1, 3, 1536, 1536])
+cls_option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
+                               [1, 3, 48, 1024])
+rec_option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
+                               [1, 3, 48, 2304])
+
+# 用户可以把TRT引擎文件保存至本地
+# det_option.set_trt_cache_file(args.det_model  + "/det_trt_cache.trt")
+# cls_option.set_trt_cache_file(args.cls_model  + "/cls_trt_cache.trt")
+# rec_option.set_trt_cache_file(args.rec_model  + "/rec_trt_cache.trt")
+
 det_model = fd.vision.ocr.DBDetector(
-    det_model_file, det_params_file, runtime_option=runtime_option)
+    det_model_file, det_params_file, runtime_option=det_option)
 cls_model = fd.vision.ocr.Classifier(
-    cls_model_file, cls_params_file, runtime_option=runtime_option)
+    cls_model_file, cls_params_file, runtime_option=cls_option)
 rec_model = fd.vision.ocr.Recognizer(
-    rec_model_file,
-    rec_params_file,
-    rec_label_file,
-    runtime_option=runtime_option)
+    rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
 
 # 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
 ppocr_v3 = fd.vision.ocr.PPOCRv3(