diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py
index 1c5c04d9db8e..0e35d8a80988 100644
--- a/vllm/model_executor/layers/fused_moe/layer.py
+++ b/vllm/model_executor/layers/fused_moe/layer.py
@@ -66,6 +66,8 @@ def apply(
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
     ) -> torch.Tensor:
         raise NotImplementedError
 
diff --git a/vllm/model_executor/layers/quantization/awq_marlin.py b/vllm/model_executor/layers/quantization/awq_marlin.py
index 473816fcc3ec..cb1d5400f3a0 100644
--- a/vllm/model_executor/layers/quantization/awq_marlin.py
+++ b/vllm/model_executor/layers/quantization/awq_marlin.py
@@ -469,6 +469,7 @@ def apply(
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
         activation: str = "silu",
     ) -> torch.Tensor:
         assert activation == "silu", "Only SiLU activation is supported."
@@ -476,6 +477,10 @@ def apply(
             raise NotImplementedError(
                 "Expert Parallelism is not supported for "
                 "fused Marlin MoE method.")
+        if apply_router_weight_on_input:
+            raise NotImplementedError(
+                "Apply router weight on input is not supported for"
+                "fused Marlin MoE method.")
 
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
diff --git a/vllm/model_executor/layers/quantization/experts_int8.py b/vllm/model_executor/layers/quantization/experts_int8.py
index d18ca55afebd..be19b80975ec 100644
--- a/vllm/model_executor/layers/quantization/experts_int8.py
+++ b/vllm/model_executor/layers/quantization/experts_int8.py
@@ -113,6 +113,7 @@ def apply(
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
         activation: str = "silu",
     ) -> torch.Tensor:
         from vllm.model_executor.layers.fused_moe import fused_experts
@@ -129,18 +130,20 @@ def apply(
             scoring_func=scoring_func,
             e_score_correction_bias=e_score_correction_bias)
 
-        return fused_experts(x,
-                             layer.w13_weight,
-                             layer.w2_weight,
-                             topk_weights=topk_weights,
-                             topk_ids=topk_ids,
-                             inplace=True,
-                             activation=activation,
-                             use_int8_w8a16=True,
-                             global_num_experts=global_num_experts,
-                             expert_map=expert_map,
-                             w1_scale=layer.w13_scale,
-                             w2_scale=layer.w2_scale)
+        return fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=activation,
+            use_int8_w8a16=True,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=expert_map,
+            w1_scale=layer.w13_scale,
+            w2_scale=layer.w2_scale)
 
     @staticmethod
     def quantizing_weight_loader(layer, weight_loader):
diff --git a/vllm/model_executor/layers/quantization/gguf.py b/vllm/model_executor/layers/quantization/gguf.py
index 9861e0a85b3f..6b499f81c55f 100644
--- a/vllm/model_executor/layers/quantization/gguf.py
+++ b/vllm/model_executor/layers/quantization/gguf.py
@@ -338,9 +338,15 @@ def apply(
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
         activation: str = "silu",
     ):
         assert activation == "silu", "Only SiLU activation is supported."
+        if apply_router_weight_on_input:
+            raise NotImplementedError(
+                "Apply router weight on input is not supported for"
+                "fused GGUF MoE method.")
+
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py
index 9f53ffc1d7f6..0615bb4ab4df 100644
--- a/vllm/model_executor/layers/quantization/gptq_marlin.py
+++ b/vllm/model_executor/layers/quantization/gptq_marlin.py
@@ -592,9 +592,14 @@ def apply(
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
         activation: str = "silu",
     ) -> torch.Tensor:
         assert activation == "silu", "Only SiLU activation is supported."
+        if apply_router_weight_on_input is not None:
+            raise NotImplementedError(
+                "Apply router weight on input is not supported for"
+                "fused Marlin MoE method.")
 
         # The input must currently be float16
         orig_dtype = x.dtype
diff --git a/vllm/model_executor/layers/quantization/moe_wna16.py b/vllm/model_executor/layers/quantization/moe_wna16.py
index 41b75c9be05a..00c4b661ef2c 100644
--- a/vllm/model_executor/layers/quantization/moe_wna16.py
+++ b/vllm/model_executor/layers/quantization/moe_wna16.py
@@ -293,6 +293,7 @@ def apply(
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
         activation: str = "silu",
     ) -> torch.Tensor:
         from vllm.model_executor.layers.fused_moe import fused_experts
@@ -312,21 +313,23 @@ def apply(
         weight_bits = self.quant_config.weight_bits
         has_zp = self.quant_config.has_zp
 
-        return fused_experts(x,
-                             layer.w13_qweight,
-                             layer.w2_qweight,
-                             topk_weights=topk_weights,
-                             topk_ids=topk_ids,
-                             inplace=True,
-                             use_int4_w4a16=weight_bits == 4,
-                             use_int8_w8a16=weight_bits == 8,
-                             global_num_experts=global_num_experts,
-                             expert_map=expert_map,
-                             w1_scale=layer.w13_scales,
-                             w2_scale=layer.w2_scales,
-                             w1_zp=layer.w13_qzeros if has_zp else None,
-                             w2_zp=layer.w2_qzeros if has_zp else None,
-                             block_shape=[0, layer.group_size])
+        return fused_experts(
+            x,
+            layer.w13_qweight,
+            layer.w2_qweight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            use_int4_w4a16=weight_bits == 4,
+            use_int8_w8a16=weight_bits == 8,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=expert_map,
+            w1_scale=layer.w13_scales,
+            w2_scale=layer.w2_scales,
+            w1_zp=layer.w13_qzeros if has_zp else None,
+            w2_zp=layer.w2_qzeros if has_zp else None,
+            block_shape=[0, layer.group_size])
 
     @staticmethod
     def get_weight_loader(layer, weight_loader):
diff --git a/vllm/model_executor/layers/quantization/quark/quark_moe.py b/vllm/model_executor/layers/quantization/quark/quark_moe.py
index bc26a455c6f2..d1146c0f039d 100644
--- a/vllm/model_executor/layers/quantization/quark/quark_moe.py
+++ b/vllm/model_executor/layers/quantization/quark/quark_moe.py
@@ -202,6 +202,8 @@ def apply(
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
     ) -> torch.Tensor:
         from vllm.model_executor.layers.fused_moe import fused_experts
 
@@ -217,16 +219,18 @@ def apply(
             scoring_func=scoring_func,
             e_score_correction_bias=e_score_correction_bias)
 
-        return fused_experts(x,
-                             layer.w13_weight,
-                             layer.w2_weight,
-                             topk_weights=topk_weights,
-                             topk_ids=topk_ids,
-                             inplace=True,
-                             use_fp8_w8a8=True,
-                             global_num_experts=global_num_experts,
-                             expert_map=expert_map,
-                             w1_scale=layer.w13_weight_scale,
-                             w2_scale=layer.w2_weight_scale,
-                             a1_scale=layer.w13_input_scale,
-                             a2_scale=layer.w2_input_scale)
+        return fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            use_fp8_w8a8=True,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=expert_map,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale)