diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py
index 3d94626e5..49ff87df9 100644
--- a/vllm/model_executor/layers/quantization/fp8.py
+++ b/vllm/model_executor/layers/quantization/fp8.py
@@ -772,10 +772,10 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             if self.allow_deep_gemm and not is_deep_gemm_e8m0_used():
                 if _is_col_major(layer.w13_weight_scale_inv):
                     layer.w13_weight_scale_inv = \
-                        get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv).contiguous()
+                        get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv)
                 if _is_col_major(layer.w2_weight_scale_inv):
                     layer.w2_weight_scale_inv = \
-                        get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv).contiguous()
+                        get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv)
 
         # If checkpoint is fp16, quantize in place.
         elif not self.quant_config.is_checkpoint_fp8_serialized:
@@ -923,10 +923,10 @@ class Fp8MoEMethod(FusedMoEMethodBase):
             # Ensure column-major TMA alignment expected by DeepGEMM.
             if _is_col_major(layer.w13_weight_scale_inv):
                 layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor(
-                    layer.w13_weight_scale_inv).contiguous()
+                    layer.w13_weight_scale_inv)
             if _is_col_major(layer.w2_weight_scale_inv):
                 layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor(
-                    layer.w2_weight_scale_inv).contiguous()
+                    layer.w2_weight_scale_inv)
 
     def select_gemm_impl(
         self,