diff --git a/nemo/export/tensorrt_lazy_compiler.py b/nemo/export/tensorrt_lazy_compiler.py
index b751ff45d74f..c7a3f0738a43 100644
--- a/nemo/export/tensorrt_lazy_compiler.py
+++ b/nemo/export/tensorrt_lazy_compiler.py
@@ -24,7 +24,7 @@
 import torch
 
 from nemo.utils.export_utils import add_casts_around_norms, replace_for_export
-from nemo.utils.import_utils import safe_import, safe_import_from
+from nemo.utils.import_utils import safe_import
 
 polygraphy, polygraphy_imported = safe_import("polygraphy")
 if polygraphy_imported:
@@ -596,17 +596,20 @@ def add_profile(id, val):
 
             # Use temporary directory for easy cleanup in case of external weights
             with tempfile.TemporaryDirectory() as tmpdir:
-                unrolled_input = unroll_input(self.input_names, input_example)
+                if export_args.get("dynamo", False):
+                    input_names = None
+                else:
+                    input_names = list(unroll_input(self.input_names, input_example).keys())
                 onnx_path = str(Path(tmpdir) / "model.onnx")
                 self.logger.info(
-                    f"Exporting to {onnx_path}:\nunrolled_inputs={list(unrolled_input.keys())}\n"
+                    f"Exporting to {onnx_path}:\n"
                     + f"output_names={self.output_names}\ninput_names={self.input_names}\nexport args: {export_args}"
                 )
                 torch.onnx.export(
                     model,
-                    input_example,
+                    (input_example,),
                     onnx_path,
-                    input_names=list(unrolled_input.keys()),
+                    input_names=input_names,
                     output_names=self.output_names,
                     **export_args,
                 )
diff --git a/tests/export/test_trt_compile.py b/tests/export/test_trt_compile.py
index 758ab3f34437..95be5f88bf01 100644
--- a/tests/export/test_trt_compile.py
+++ b/tests/export/test_trt_compile.py
@@ -16,10 +16,9 @@
 from typing import List
 
 import torch
-from parameterized import parameterized
 
 from nemo.export import trt_compile
-from nemo.utils.import_utils import safe_import, safe_import_from
+from nemo.utils.import_utils import safe_import
 
 trt, trt_imported = safe_import("tensorrt")
 torch_tensorrt, torch_trt_imported = safe_import("torch_tensorrt")
@@ -67,8 +66,11 @@ def test_torch_trt(self):
         x = torch.randn(1, 16).to("cuda")
 
         with tempfile.TemporaryDirectory() as tempdir:
-            args = {"method": "torch_trt"}
-            input_example = x
+            args = {
+                "method": "torch_trt",
+                "dynamic_batchsize": [1, 4, 8],
+            }
+            input_example = (x,)
             output_example = model(*input_example)
             trt_compile(
                 model,
@@ -81,11 +83,49 @@ def test_torch_trt(self):
             self.assertIsNotNone(model._trt_compiler.engine)
             torch.testing.assert_close(trt_output, output_example, rtol=0.01, atol=0.01)
 
+    def test_profiles(self):
+        model = ListAdd().cuda()
+
+        with torch.no_grad(), tempfile.TemporaryDirectory() as tmpdir:
+            args = {
+                "export_args": {
+                    "dynamo": False,
+                },
+                "input_profiles": [
+                    {
+                        "x_0": [[1, 8], [2, 16], [2, 32]],
+                        "x_1": [[1, 8], [2, 16], [2, 32]],
+                        "x_2": [[1, 8], [2, 16], [2, 32]],
+                        "y": [[1, 8], [2, 16], [2, 32]],
+                        "z": [[1, 8], [1, 16], [1, 32]],
+                    }
+                ],
+                "output_lists": [[-1], [2], []],
+            }
+            x = torch.randn(1, 16).to("cuda")
+            y = torch.randn(1, 16).to("cuda")
+            z = torch.randn(1, 16).to("cuda")
+            input_example = ([x, y, z], y.clone(), z.clone())
+            output_example = model(*input_example)
+            trt_compile(
+                model,
+                f"{tmpdir}/test_dynamo_trt",
+                args=args,
+            )
+            self.assertIsNone(model._trt_compiler.engine)
+            trt_output = model(*input_example)
+            # Check that lazy TRT build succeeded
+            self.assertIsNotNone(model._trt_compiler.engine)
+            torch.testing.assert_close(trt_output, output_example, rtol=0.01, atol=0.01)
+
     def test_lists(self):
         model = ListAdd().cuda()
 
         with torch.no_grad(), tempfile.TemporaryDirectory() as tmpdir:
             args = {
+                "export_args": {
+                    "dynamo": True,
+                },
                 "output_lists": [[-1], [2], []],
             }
             x = torch.randn(1, 16).to("cuda")