[Fix] Restore online MXFP8 quantization for linear layers (#32953)

python/sglang/srt/layers/quantization/fp8.py

@@ -617,10 +617,6 @@ class Fp8LinearMethod(LinearMethodBase):
                 layer.register_parameter("input_scale", scale)
             else:
                 layer.register_parameter("input_scale", None)
-        elif use_mxfp8:
-            raise ValueError(
-                "MXFP8 requires fp8-serialized checkpoint for linear layers."
-            )
 
     def create_weights(
         self,

python/sglang/srt/models/nemotron_h.py

@@ -261,15 +261,17 @@ class NemotronHMoE(nn.Module):
             self.fc1_latent_proj = None
             self.fc2_latent_proj = None
 
-        self.use_min_latency_fc1_gemm = (
-            self.use_latent_moe
-            and self.fc1_latent_proj is not None
-            and _is_cuda
-            and fused_a_gemm_weight_eligible(self.fc1_latent_proj)
-        )
+        self._use_min_latency_fc1_gemm: bool | None = None
 
     def _apply_fc1_latent_proj(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        if self.use_min_latency_fc1_gemm:
+        if self._use_min_latency_fc1_gemm is None:
+            self._use_min_latency_fc1_gemm = (
+                self.use_latent_moe
+                and self.fc1_latent_proj is not None
+                and _is_cuda
+                and fused_a_gemm_weight_eligible(self.fc1_latent_proj)
+            )
+        if self._use_min_latency_fc1_gemm:
             return linear_with_fused_a_gemm(self.fc1_latent_proj, hidden_states)
         return self.fc1_latent_proj(hidden_states)[0]