diff --git a/vllm/model_executor/layers/quantization/mxfp4.py b/vllm/model_executor/layers/quantization/mxfp4.py
index 7940b359a150..0f69a18a1f3f 100644
--- a/vllm/model_executor/layers/quantization/mxfp4.py
+++ b/vllm/model_executor/layers/quantization/mxfp4.py
@@ -1145,7 +1145,7 @@ class IpexMxfp4MoEMethod(Mxfp4MoEMethod):
     ) -> torch.Tensor:
         assert activation == "swigluoai", (
             "Only swiglu_oai activation is supported for IPEX MXFP4 MoE"
-        )  # noqa:
+        )
         hidden_size_pad = round_up(self.original_hidden_size, 128)
         x_pad = torch.nn.functional.pad(x, (0, hidden_size_pad - x.size(-1)))
         hidden_states = layer.ipex_fusion(
diff --git a/vllm/platforms/xpu.py b/vllm/platforms/xpu.py
index 3a8e174f2b74..0309ae0fe962 100644
--- a/vllm/platforms/xpu.py
+++ b/vllm/platforms/xpu.py
@@ -64,8 +64,6 @@ class XPUPlatform(Platform):
 
         if use_sparse:
             raise NotImplementedError("Sparse Attention is not supported on XPU.")
-        if not use_v1:
-            raise ValueError("XPU backend only supports V1.")
         if selected_backend == AttentionBackendEnum.TRITON_ATTN:
             logger.info_once("Using Triton backend.")
             return AttentionBackendEnum.TRITON_ATTN.get_path()