diff --git a/paddle/fluid/inference/tensorrt/convert/gather_nd_op.cc b/paddle/fluid/inference/tensorrt/convert/gather_nd_op.cc
index c6ed96a674d1342b5986f0565d23c0410efa17aa..2b926ccdf122146b2b81eeb8ecd451954736cae2 100644
--- a/paddle/fluid/inference/tensorrt/convert/gather_nd_op.cc
+++ b/paddle/fluid/inference/tensorrt/convert/gather_nd_op.cc
@@ -24,13 +24,24 @@ class GatherNdOpConverter : public OpConverter {
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope,
                   bool test_mode) override {
-    VLOG(4) << "convert a paddle gather_nd op to tensorrt gather_nd plugin";
     framework::OpDesc op_desc(op, nullptr);
+    auto input = engine_->GetITensor(op_desc.Input("X")[0]);
+    auto index = engine_->GetITensor(op_desc.Input("Index")[0]);
+    auto output_name = op_desc.Output("Out")[0];
+
+    // AddGatherV2 is supported by the trt version of 8.2.
+#if IS_TRT_VERSION_GE(8200)
+    VLOG(3) << "convert gather_nd op to tensorrt gather_nd layer";
+
+    auto layer = TRT_ENGINE_ADD_LAYER(
+        engine_, GatherV2, *input, *index, nvinfer1::GatherMode::kND);
+    layer->setNbElementWiseDims(0);
+    RreplenishLayerAndOutput(layer, "gather_nd", {output_name}, test_mode);
+#else
+    VLOG(4) << "convert a paddle gather_nd op to tensorrt gather_nd plugin";
 
     // Declare inputs
     std::vector<nvinfer1::ITensor*> inputs;
-    auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
-    auto* index = engine_->GetITensor(op_desc.Input("Index")[0]);
     inputs.emplace_back(input);
     inputs.emplace_back(index);
 
@@ -41,7 +52,6 @@ class GatherNdOpConverter : public OpConverter {
     layer = engine_->AddDynamicPlugin(inputs.data(), inputs.size(), plugin);
 
     std::string layer_name = "gather_nd (Output: ";
-    auto output_name = op_desc.Output("Out")[0];
     layer->getOutput(0)->setName(output_name.c_str());
     engine_->SetITensor(output_name, layer->getOutput(0));
     layer_name += output_name;
@@ -49,6 +59,7 @@ class GatherNdOpConverter : public OpConverter {
       engine_->DeclareOutput(output_name);
     }
     layer->setName((layer_name + ")").c_str());
+#endif
   }
 };
 
diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc
index 24d5209baaa4ffae9368f0146f483736570ace7c..363b3132a1536b64862433fd5edfc51d483612fc 100644
--- a/paddle/fluid/inference/tensorrt/op_teller.cc
+++ b/paddle/fluid/inference/tensorrt/op_teller.cc
@@ -566,9 +566,8 @@ struct SimpleOpTypeSetTeller : public Teller {
                    "the pass.";
         return false;
       }
-      auto x_var_name = desc.Input("X")[0];
+
       auto index_var_name = desc.Input("Index")[0];
-      auto* x_var_desc = block->FindVar(x_var_name);
       auto* index_var_desc = block->FindVar(index_var_name);
 
       // The index input must be int32 datatype.
@@ -578,6 +577,9 @@ struct SimpleOpTypeSetTeller : public Teller {
         return false;
       }
 
+#if IS_TRT_VERSION_LT(8200)
+      auto x_var_name = desc.Input("X")[0];
+      auto* x_var_desc = block->FindVar(x_var_name);
       const auto index_shape = index_var_desc->GetShape();
       const auto x_shape = x_var_desc->GetShape();
       if (x_shape.size() <= 2) {
@@ -591,6 +593,7 @@ struct SimpleOpTypeSetTeller : public Teller {
                 << " ] not equal to x dims size [" << x_shape.size() << "]";
         return false;
       }
+#endif
     }
 
     if (op_type == "anchor_generator") {
diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py
index 10d3048a26cbe552db622a3d2e241d5de07a332e..8b32e5516b97e6976dd5eeb9f8b651e34809d5c6 100644
--- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py
+++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py
@@ -69,11 +69,11 @@ class TrtConvertGatherNdTest_dim_4_1(TrtLayerAutoScanTest):
     ) -> (paddle_infer.Config, List[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
-                "input_data": [1, 8, 8, 8],
+                "input_data": [2, 32, 64, 64],
                 "index_data": [1],
             }
             self.dynamic_shape.max_input_shape = {
-                "input_data": [4, 32, 64, 64],
+                "input_data": [2, 32, 64, 64],
                 "index_data": [1],
             }
             self.dynamic_shape.opt_input_shape = {
@@ -159,11 +159,11 @@ class TrtConvertGatherNdTest_dim_4_1_2(TrtLayerAutoScanTest):
     ) -> (paddle_infer.Config, List[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
-                "input_data": [1, 8, 8, 8],
+                "input_data": [2, 32, 64, 64],
                 "index_data": [2],
             }
             self.dynamic_shape.max_input_shape = {
-                "input_data": [4, 32, 64, 64],
+                "input_data": [2, 32, 64, 64],
                 "index_data": [2],
             }
             self.dynamic_shape.opt_input_shape = {
@@ -249,11 +249,11 @@ class TrtConvertGatherNdTest_dim_4_2(TrtLayerAutoScanTest):
     ) -> (paddle_infer.Config, List[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
-                "input_data": [1, 8, 8, 8],
+                "input_data": [2, 32, 64, 64],
                 "index_data": [2, 2],
             }
             self.dynamic_shape.max_input_shape = {
-                "input_data": [4, 32, 64, 64],
+                "input_data": [2, 32, 64, 64],
                 "index_data": [2, 2],
             }
             self.dynamic_shape.opt_input_shape = {
@@ -339,11 +339,11 @@ class TrtConvertGatherNdTest_dim_4_3(TrtLayerAutoScanTest):
     ) -> (paddle_infer.Config, List[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
-                "input_data": [1, 8, 8, 8],
+                "input_data": [2, 32, 64, 64],
                 "index_data": [2, 2, 4],
             }
             self.dynamic_shape.max_input_shape = {
-                "input_data": [4, 32, 64, 64],
+                "input_data": [2, 32, 64, 64],
                 "index_data": [2, 2, 4],
             }
             self.dynamic_shape.opt_input_shape = {
@@ -429,15 +429,15 @@ class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest):
     ) -> (paddle_infer.Config, List[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
-                "input_data": [1, 4],
+                "input_data": [2, 32],
                 "index_data": [2, 2],
             }
             self.dynamic_shape.max_input_shape = {
-                "input_data": [4, 64],
+                "input_data": [2, 32],
                 "index_data": [2, 2],
             }
             self.dynamic_shape.opt_input_shape = {
-                "input_data": [2, 8],
+                "input_data": [2, 32],
                 "index_data": [2, 2],
             }
 
@@ -521,15 +521,15 @@ class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest):
     ) -> (paddle_infer.Config, List[int], float):
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {
-                "input_data": [1, 4, 4],
-                "index_data": [1, 1, 1],
+                "input_data": [16, 32, 256],
+                "index_data": [2, 2, 2],
             }
             self.dynamic_shape.max_input_shape = {
-                "input_data": [16, 64, 512],
-                "index_data": [4, 2, 4],
+                "input_data": [16, 32, 256],
+                "index_data": [2, 2, 2],
             }
             self.dynamic_shape.opt_input_shape = {
-                "input_data": [2, 8, 64],
+                "input_data": [16, 32, 256],
                 "index_data": [2, 2, 2],
             }