diff --git a/py/torch_tensorrt/dynamo/_exporter.py b/py/torch_tensorrt/dynamo/_exporter.py
index a329bbe28a..a7337f4f8e 100644
--- a/py/torch_tensorrt/dynamo/_exporter.py
+++ b/py/torch_tensorrt/dynamo/_exporter.py
@@ -98,6 +98,13 @@ def lift(
     )
     assert fake_mode is not None
 
+    # This map stores the names of outputs (old to new)
+    # This is necessary to track because the output names can be changed when
+    # we convert graph constants to placeholder inputs below.
+    output_names = {}
+    for output_spec in graph_signature.output_specs:
+        output_names[output_spec.arg.name] = output_spec.arg.name
+
     # Locate the user input to insert new placeholders before them
     first_user_input = None
     for node in gm.graph.nodes:
@@ -139,9 +146,8 @@ def lift(
             # Replace get_attr nodes with placeholder nodes and copy metadata.
             with gm.graph.inserting_before(first_user_input):
                 # Ensure name doesn't contain period as it is used for submodules
-                const_placeholder_node = gm.graph.placeholder(
-                    node.target.replace(".", "_")
-                )
+                const_placeholder_name = node.target.replace(".", "_")
+                const_placeholder_node = gm.graph.placeholder(const_placeholder_name)
                 # Copy the node meta into this new placeholder node
                 const_placeholder_node.meta = node.meta
 
@@ -157,6 +163,12 @@ def lift(
                 node.replace_all_uses_with(const_placeholder_node)
                 gm.graph.erase_node(node)
 
+                # Verify if the const_placeholder being added is one of the output nodes
+                # This happens if there is just a single static arange op in the graph
+                # https://github.com/pytorch/TensorRT/issues/3189
+                if const_placeholder_name in output_names:
+                    output_names[const_placeholder_name] = const_placeholder_node.name
+
                 # Add these parameters/buffers/constants to the existing graph signature
                 # before user inputs. These specs are looked up in the state_dict during ExportedProgram creation.
                 input_spec_arg = TensorArgument(name=const_placeholder_node.name)
@@ -174,6 +186,11 @@ def lift(
                 )
                 non_user_input_idx += 1
 
+    # Update output_specs with modified names. This only gets updated if the graph getattr nodes (weights)
+    # are also the outputs of the graph
+    for output_spec in graph_signature.output_specs:
+        output_spec.arg.name = output_names[output_spec.arg.name]
+
     gm.graph.eliminate_dead_code()
     gm.graph.lint()
 
diff --git a/tests/py/dynamo/models/test_export_serde.py b/tests/py/dynamo/models/test_export_serde.py
index 146cc2addf..470da496ba 100644
--- a/tests/py/dynamo/models/test_export_serde.py
+++ b/tests/py/dynamo/models/test_export_serde.py
@@ -381,6 +381,64 @@ def forward(self, x):
         )
 
 
+@pytest.mark.unit
+def test_arange_export(ir):
+    """
+    This tests export save and load functionality on a arange static graph
+    Here the arange output is a static constant (which is registered as input to the graph)
+    in the exporter.
+    """
+
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+
+        def forward(self, x):
+            x_embed = torch.arange(
+                1, x.shape[-1] + 1, dtype=torch.float32, device=x.device
+            )
+            return x_embed
+
+    model = MyModule().eval().cuda()
+    input = torch.randn((1, 1, 128, 128)).to("cuda")
+
+    compile_spec = {
+        "inputs": [
+            torchtrt.Input(
+                input.shape, dtype=torch.float, format=torch.contiguous_format
+            )
+        ],
+        "ir": ir,
+        "min_block_size": 1,
+        "cache_built_engines": False,
+        "reuse_cached_engines": False,
+    }
+
+    exp_program = torchtrt.dynamo.trace(model, **compile_spec)
+    trt_module = torchtrt.dynamo.compile(exp_program, **compile_spec)
+
+    torchtrt.save(trt_module, trt_ep_path, inputs=[input])
+
+    deser_trt_module = torchtrt.load(trt_ep_path).module()
+    outputs_pyt = model(input)
+    outputs_trt = trt_module(input)
+
+    for idx in range(len(outputs_pyt)):
+        cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
+        assertions.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"test_arange_export TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+    outputs_trt_deser = deser_trt_module(input)
+    for idx in range(len(outputs_pyt)):
+        cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
+        assertions.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"test_arange_export deserialized TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+
 @pytest.mark.unit
 def test_save_load_ts(ir):
     """