diff --git a/src/runtime/contrib/tensorrt/tensorrt_builder.cc b/src/runtime/contrib/tensorrt/tensorrt_builder.cc
index d8182b0e8378..08ac2ae0ec45 100644
--- a/src/runtime/contrib/tensorrt/tensorrt_builder.cc
+++ b/src/runtime/contrib/tensorrt/tensorrt_builder.cc
@@ -163,10 +163,19 @@ TensorRTEngineAndContext TensorRTBuilder::BuildEngine() {
     auto profile = builder_->createOptimizationProfile();
     for (int i = 0; i < network_->getNbInputs(); ++i) {
       auto name = network_->getInput(i)->getName();
-      auto dims = network_->getInput(i)->getDimensions();
-      profile->setDimensions(name, nvinfer1::OptProfileSelector::kMIN, dims);
+      const uint32_t entry_id = entry_id_map_[name];
+      std::vector<int64_t> shape(data_entry_[entry_id]->shape,
+                                 data_entry_[entry_id]->shape + data_entry_[entry_id]->ndim);
+      auto dims = VectorToTrtDims(shape);
+
       profile->setDimensions(name, nvinfer1::OptProfileSelector::kOPT, dims);
       profile->setDimensions(name, nvinfer1::OptProfileSelector::kMAX, dims);
+      // Set minimum batch size to 1 when dynamic batching is used.
+      if (network_->getInput(i)->getDimensions().nbDims >= 1 &&
+          network_->getInput(i)->getDimensions().d[0] == -1) {
+        dims.d[0] = 1;
+      }
+      profile->setDimensions(name, nvinfer1::OptProfileSelector::kMIN, dims);
     }
     config_->addOptimizationProfile(profile);
   }
diff --git a/src/runtime/contrib/tensorrt/tensorrt_runtime.cc b/src/runtime/contrib/tensorrt/tensorrt_runtime.cc
index 6358e59ce3bc..5562f853383c 100644
--- a/src/runtime/contrib/tensorrt/tensorrt_runtime.cc
+++ b/src/runtime/contrib/tensorrt/tensorrt_runtime.cc
@@ -140,6 +140,12 @@ class TensorRTRuntime : public JSONRuntimeBase {
           const std::string name = nodes_[nid].GetOpName() + "_" + std::to_string(j);
           int binding_index = engine->getBindingIndex(name.c_str());
           ICHECK_NE(binding_index, -1);
+          if (!use_implicit_batch_) {
+            std::vector<int64_t> shape(data_entry_[eid]->shape,
+                                       data_entry_[eid]->shape + data_entry_[eid]->ndim);
+            auto dims = VectorToTrtDims(shape);
+            ICHECK(context->setBindingDimensions(binding_index, dims));
+          }
           if (data_entry_[eid]->device.device_type == kDLCUDA) {
             bindings[binding_index] = data_entry_[eid]->data;
           } else {
@@ -300,7 +306,7 @@ class TensorRTRuntime : public JSONRuntimeBase {
     helper.DeclareField("inputs", &engine_and_context.inputs);
     helper.DeclareField("outputs", &engine_and_context.outputs);
     helper.ReadAllFields(&reader);
-    const int batch_size = 1;
+    const int batch_size = GetBatchSize();
     trt_engine_cache_[std::make_pair(symbol_name_, batch_size)] = engine_and_context;
     return true;
   }
diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py
index 59f1c3aa4d68..3f57df5a5f4a 100644
--- a/tests/python/contrib/test_tensorrt.py
+++ b/tests/python/contrib/test_tensorrt.py
@@ -1251,33 +1251,35 @@ def test_tensorrt_dynamic_batch_conv():
     x_data = np.ones([max(batches_to_test)] + list(x_shape)[1:]).astype("float32")
     k_shape = (16, 32, 3, 3)
     params = {"kernel": np.random.uniform(-1, 1, k_shape).astype("float32")}
-    result_arr = [{"cuda": {}, "llvm": {}} for _ in range(len(batches_to_test))]
-    for use_trt in [True, False]:
-        x = relay.var("x", shape=x_shape, dtype="float32")
-        kernel = relay.var("kernel", shape=k_shape, dtype="float32")
-        out = relay.nn.conv2d(x, kernel, channels=16, kernel_size=(3, 3), groups=1)
-        f = relay.Function([x, kernel], out)
-        mod = tvm.IRModule()
-        mod["main"] = f
-        if use_trt:
-            mod, _ = tensorrt.partition_for_tensorrt(mod, params)
-
+    for use_implicit_batch in [True, False]:
+        result_arr = [{"cuda": {}, "llvm": {}} for _ in range(len(batches_to_test))]
+        for use_trt in [True, False]:
+            x = relay.var("x", shape=x_shape, dtype="float32")
+            kernel = relay.var("kernel", shape=k_shape, dtype="float32")
+            out = relay.nn.conv2d(x, kernel, channels=16, kernel_size=(3, 3), groups=1)
+            f = relay.Function([x, kernel], out)
+            mod = tvm.IRModule()
+            mod["main"] = f
+            if use_trt:
+                mod, config = tensorrt.partition_for_tensorrt(
+                    mod, params, use_implicit_batch=use_implicit_batch
+                )
+            if not skip_runtime_test():
+                for target in ["llvm", "cuda"]:
+                    with tvm.transform.PassContext(
+                        opt_level=3, config={"relay.ext.tensorrt.options": config}
+                    ):
+                        relay_exec = relay.create_executor(
+                            "vm", mod=mod, device=tvm.device(target), target=target
+                        )
+                    for i, batch_size in enumerate(batches_to_test):
+                        result_arr[i][target][use_trt] = relay_exec.evaluate()(
+                            x_data[:batch_size, ...], **params
+                        )
         if not skip_runtime_test():
-            for target in ["llvm", "cuda"]:
-                with relay.build_config(opt_level=3):
-                    relay_exec = relay.create_executor(
-                        "vm", mod=mod, device=tvm.cpu(0), target="llvm"
-                    )
-
-                for i, batch_size in enumerate(batches_to_test):
-                    result_arr[i][target][use_trt] = relay_exec.evaluate()(
-                        x_data[:batch_size, ...], **params
-                    )
-
-    if not skip_runtime_test():
-        for i in range(len(batches_to_test)):
-            for target in ["llvm", "cuda"]:
-                assert_result_dict_holds(result_arr[i][target])
+            for i in range(len(batches_to_test)):
+                for target in ["llvm", "cuda"]:
+                    assert_result_dict_holds(result_arr[i][target])
 
 
 def test_maskrcnn_resnet50() -> None: