diff --git a/tpu_inference/layers/vllm/quantization/fp8.py b/tpu_inference/layers/vllm/quantization/fp8.py
index ae22bf4044..86ecc60d47 100644
--- a/tpu_inference/layers/vllm/quantization/fp8.py
+++ b/tpu_inference/layers/vllm/quantization/fp8.py
@@ -22,7 +22,8 @@
 from torchax.interop import jax_view, torch_view
 from torchax.ops.mappings import t2j
 from vllm.attention.layer import Attention
-from vllm.model_executor.layers.fused_moe import FusedMoE, FusedMoERouter
+from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
+                                                  FusedMoERouter)
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization import \
     register_quantization_config
@@ -242,7 +243,12 @@ def __init__(self,
                  layer: torch.nn.Module,
                  mesh: Mesh,
                  ep_axis_name: str = "model"):
-        super().__init__(quant_config, layer)
+        FusedMoEMethodBase.__init__(self, layer.moe_config)
+        self.quant_config = quant_config
+        self.weight_block_size = self.quant_config.weight_block_size
+        self.block_quant: bool = self.weight_block_size is not None
+        self.weight_scale_name = ("weight_scale_inv"
+                                  if self.block_quant else "weight_scale")
 
         self.mesh = mesh
         self.moe_backend = select_moe_backend(self.moe)
@@ -251,6 +257,10 @@ def __init__(self,
         if self.moe_backend == FusedMoEBackend.FUSED_MOE:
             self.extra_backend_kwargs = dict(ep_axis_name=ep_axis_name, )
 
+    def get_fused_moe_quant_config(self, layer: torch.nn.Module):
+        # Override quant config fused moe
+        pass
+
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         assert isinstance(layer, FusedMoE)