diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py
index b2548e66827d..828111dc299e 100644
--- a/vllm/model_executor/layers/quantization/utils/fp8_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/fp8_utils.py
@@ -911,15 +911,15 @@ def maybe_post_process_fp8_weight_block(layer: torch.nn.Module,
     # On Blackwell or Hopper, if E8M0 for DeepGemm is used, we need to
     # requantize the weight and input to the specific scale
     # at the same time.
-    if is_deep_gemm_e8m0_used():
+    should_use_deepgemm = should_use_deepgemm_for_fp8_linear(
+        layer.orig_dtype, layer.weight)
+    if is_deep_gemm_e8m0_used() and should_use_deepgemm:
         block_sz = tuple(layer.weight_block_size)
         requant_weight_ue8m0_inplace(layer.weight.data,
                                      layer.weight_scale.data, block_sz)
     # SM90 Block FP8 CUTLASS requires row-major weight scales
     elif (current_platform.is_device_capability(90)
-          and cutlass_block_fp8_supported
-          and not should_use_deepgemm_for_fp8_linear(torch.bfloat16,
-                                                     layer.weight)):
+          and cutlass_block_fp8_supported and not should_use_deepgemm):
         layer.weight_scale = torch.nn.Parameter(
             layer.weight_scale.data.T.contiguous(), requires_grad=False)