diff --git a/vllm/model_executor/layers/quantization/modelopt.py b/vllm/model_executor/layers/quantization/modelopt.py
index adce598c4ff1f..9d4e453ffc545 100644
--- a/vllm/model_executor/layers/quantization/modelopt.py
+++ b/vllm/model_executor/layers/quantization/modelopt.py
@@ -891,7 +891,11 @@ class ModelOptNvFp4LinearMethod(LinearMethodBase):
         assert (layer.weight_scale.dtype == torch.float8_e4m3fn), (
             "Weight Block scale must be represented as FP8-E4M3")
 
-        if self.backend == "flashinfer-trtllm":
+        if self.backend == "marlin":
+            prepare_fp4_layer_for_marlin(layer)
+            del layer.alpha
+            del layer.input_scale
+        elif self.backend == "flashinfer-trtllm":
             # FlashInfer TRTLLM FP4 GEMM requires a different weight layout.
             # FlashInfer provides nvfp4_quantize to quantize + shuffle the
             # layout but we use our own quantization so we have to call
@@ -916,11 +920,6 @@ class ModelOptNvFp4LinearMethod(LinearMethodBase):
                                            requires_grad=False)
             layer.weight = Parameter(layer.weight.data, requires_grad=False)
 
-            if self.backend == "marlin":
-                prepare_fp4_layer_for_marlin(layer)
-                del layer.alpha
-                del layer.input_scale
-
     def apply(
         self,
         layer: torch.nn.Module,
@@ -1312,6 +1311,13 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
             del layer.w2_weight_scale
             del layer.w13_weight
             del layer.w13_weight_scale
+        elif self.use_marlin:
+            # Marlin processing
+            prepare_moe_fp4_layer_for_marlin(layer)
+            del layer.g1_alphas
+            del layer.g2_alphas
+            del layer.w13_input_scale_quant
+            del layer.w2_input_scale_quant
         else:
             # Non-TRT-LLM processing (Cutlass or non-flashinfer)
             assert (layer.w13_weight_scale.shape[2] % 16 == 0), (
@@ -1333,13 +1339,6 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
             layer.w2_weight = Parameter(layer.w2_weight.data,
                                         requires_grad=False)
 
-        if self.use_marlin:
-            prepare_moe_fp4_layer_for_marlin(layer)
-            del layer.g1_alphas
-            del layer.g2_alphas
-            del layer.w13_input_scale_quant
-            del layer.w2_input_scale_quant
-
     def apply(
         self,
         layer: torch.nn.Module,