diff --git a/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py b/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
index 74096ef6e..5f4607657 100644
--- a/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
@@ -69,54 +69,11 @@ class TrtLlmFp8ExpertsBase:
         """Does not support non-gated MoE (i.e. Nanotron-3-Nano)."""
         return True
 
-    @staticmethod
-    def _supports_quant_scheme(
-        weight_key: QuantKey | None,
-        activation_key: QuantKey | None,
-    ) -> bool:
-        """Supports Fp8 per-tensor, Fp8 block, and MXFP8."""
-        SUPPORTED_W_A = [
-            (kFp8Static128BlockSym, kFp8Dynamic128Sym),
-            (kFp8StaticTensorSym, kFp8StaticTensorSym),
-            (kMxfp8Static, kMxfp8Dynamic),
-        ]
-        return (weight_key, activation_key) in SUPPORTED_W_A
-
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
         """Supports only SiLU and RELU^2 non-gated activation."""
         return activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
 
-    @staticmethod
-    def _supports_routing_method(
-        routing_method: RoutingMethodType,
-        weight_key: QuantKey | None,
-        activation_key: QuantKey | None,
-    ) -> bool:
-        """Monolithic kernels need to express router support."""
-        # NOTE(dbari): TopK routing could also be enabled, but need to validate models
-        # NOTE(dbari): Default is not implemented and should not be enabled until it is
-        if (weight_key, activation_key) in [
-            (kFp8Static128BlockSym, kFp8Dynamic128Sym),
-            (kMxfp8Static, kMxfp8Dynamic),
-        ]:
-            # NOTE(rob): potentially allow others here. This is a conservative list.
-            return routing_method in [
-                RoutingMethodType.DeepSeekV3,
-                RoutingMethodType.Renormalize,
-                RoutingMethodType.RenormalizeNaive,
-            ]
-        elif (weight_key, activation_key) == (kFp8StaticTensorSym, kFp8StaticTensorSym):
-            # NOTE(dbari): as above, potentially allow others here.
-            return routing_method in [
-                RoutingMethodType.DeepSeekV3,
-                RoutingMethodType.Llama4,
-                RoutingMethodType.Renormalize,
-                RoutingMethodType.RenormalizeNaive,
-            ]
-        else:
-            raise ValueError("Unsupported quantization scheme.")
-
     @staticmethod
     def _supports_parallel_config(moe_parallel_config: FusedMoEParallelConfig) -> bool:
         """Monolithic kernel so only use with naive DP/EP and TP."""
@@ -125,22 +82,6 @@ class TrtLlmFp8ExpertsBase:
             or moe_parallel_config.use_naive_all2all_kernels
         ) and not moe_parallel_config.enable_eplb
 
-    @staticmethod
-    def _supports_router_logits_dtype(
-        router_logits_dtype: torch.dtype | None,
-        routing_method: RoutingMethodType,
-    ) -> bool:
-        """
-        The FlashInfer TRTLLM FP8 kernel expects bfloat16 router_logits by default.
-        Only DeepSeekV3 routing supports float32 router_logits (which is converted
-        internally in the kernel).
-        """
-        if router_logits_dtype == torch.float32:
-            # Only DeepSeekV3 routing handles float32 logits
-            # https://github.com/flashinfer-ai/flashinfer/issues/2469
-            return routing_method == RoutingMethodType.DeepSeekV3
-        return True
-
     def supports_chunking(self) -> bool:
         return False
 
@@ -306,6 +247,22 @@ class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolit
         ]
         return (weight_key, activation_key) in SUPPORTED_W_A
 
+    @staticmethod
+    def _supports_router_logits_dtype(
+        router_logits_dtype: torch.dtype | None,
+        routing_method: RoutingMethodType,
+    ) -> bool:
+        """
+        The FlashInfer TRTLLM FP8 kernel expects bfloat16 router_logits by default.
+        Only DeepSeekV3 routing supports float32 router_logits (which is converted
+        internally in the kernel).
+        """
+        if router_logits_dtype == torch.float32:
+            # Only DeepSeekV3 routing handles float32 logits
+            # https://github.com/flashinfer-ai/flashinfer/issues/2469
+            return routing_method == RoutingMethodType.DeepSeekV3
+        return True
+
     @staticmethod
     def _supports_routing_method(
         routing_method: RoutingMethodType,