diff --git a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
index ab9629b0db..42880646c3 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/elementwise/base.py
@@ -2,7 +2,6 @@
 import warnings
 from typing import Any, Callable, Optional, Union
 
-import numpy as np
 import tensorrt as trt
 import torch
 from torch.fx.node import Target
@@ -103,6 +102,14 @@ def convert_binary_elementwise(
         rhs_dtype = rhs_val.dtype
         is_rhs_trt_tensor = True
 
+    # Handle scalar tensor type promotion for elementwise operations
+    # When one operand is a scalar tensor (0-dimensional), promote its dtype to match the other operand
+    # This ensures consistent type handling in Torch elementwise operations
+    if isinstance(lhs_val, torch.Tensor) and len(lhs_val.shape) == 0:
+        lhs_dtype = rhs_dtype
+    if isinstance(rhs_val, torch.Tensor) and len(rhs_val.shape) == 0:
+        rhs_dtype = lhs_dtype
+
     if not is_lhs_trt_tensor and not is_rhs_trt_tensor:
         warnings.warn(
             f"Both operands of the binary elementwise op {name} "
diff --git a/py/torch_tensorrt/dynamo/conversion/impl/matmul.py b/py/torch_tensorrt/dynamo/conversion/impl/matmul.py
index 83ea3dd99b..dfc917bc00 100644
--- a/py/torch_tensorrt/dynamo/conversion/impl/matmul.py
+++ b/py/torch_tensorrt/dynamo/conversion/impl/matmul.py
@@ -8,7 +8,6 @@
 from torch_tensorrt.dynamo.conversion._ConversionContext import ConversionContext
 from torch_tensorrt.dynamo.conversion.converter_utils import (
     broadcast,
-    cast_trt_tensor,
     get_trt_tensor,
     set_layer_name,
 )
@@ -48,16 +47,6 @@ def matrix_multiply(
     input, other = broadcast(
         ctx, input, other, f"{name}_input", f"{name}_other", preset_diff
     )
-    if ctx.net.get_flag(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED):
-        promoted_type = _enums.dtype._from(
-            torch.promote_types(
-                _enums.dtype._from(input.dtype).to(torch.dtype),
-                _enums.dtype._from(other.dtype).to(torch.dtype),
-            )
-        )
-        trt_promoted_type = promoted_type.to(trt.DataType)
-        input = cast_trt_tensor(ctx, input, trt_promoted_type, f"{name}_input_casted")
-        other = cast_trt_tensor(ctx, other, trt_promoted_type, f"{name}_other_casted")
 
     layer = ctx.net.add_matrix_multiply(input, input_matrix_op, other, other_matrix_op)
     set_layer_name(layer, target, name, source_ir)