diff --git a/py/torch_tensorrt/dynamo/conversion/impl/cat.py b/py/torch_tensorrt/dynamo/conversion/impl/cat.py
index 0a52d1efd5..096bc1aa24 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/cat.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/cat.py
@@ -35,24 +35,25 @@ def cat(
             )
         trt_inputs.append(each_input)
 
-    # Cast to promoted type for all inputs
-    promoted_type = trt_inputs[0].dtype
-    for each_input in trt_inputs[1:]:
-        promoted_type = _enums.dtype._from(
-            torch.promote_types(
-                _enums.dtype._from(promoted_type).to(torch.dtype),
-                _enums.dtype._from(each_input.dtype).to(torch.dtype),
+    if len(trt_inputs) > 1:
+        # Cast to promoted type for all inputs
+        promoted_type = trt_inputs[0].dtype
+        for each_input in trt_inputs[1:]:
+            promoted_type = _enums.dtype._from(
+                torch.promote_types(
+                    _enums.dtype._from(promoted_type).to(torch.dtype),
+                    _enums.dtype._from(each_input.dtype).to(torch.dtype),
+                )
             )
-        )
+        trt_promoted_type = promoted_type.to(trt.DataType)
 
-    trt_promoted_type = promoted_type.to(trt.DataType)
-    trt_casted_inputs = []
-    for i, each_input in enumerate(trt_inputs):
-        casted_input = cast_trt_tensor(
-            ctx, each_input, trt_promoted_type, f"{name}_input_casted_{i}"
-        )
-        trt_casted_inputs.append(casted_input)
-    trt_inputs = trt_casted_inputs
+        trt_casted_inputs = []
+        for i, each_input in enumerate(trt_inputs):
+            casted_input = cast_trt_tensor(
+                ctx, each_input, trt_promoted_type, f"{name}_input_casted_{i}"
+            )
+            trt_casted_inputs.append(casted_input)
+        trt_inputs = trt_casted_inputs
 
     concat_layer = ctx.net.add_concatenation(trt_inputs)
     dim = get_positive_dim(dim, len(trt_inputs[0].shape))