diff --git a/vllm/model_executor/layers/fused_moe/experts/flashinfer_b12x_moe.py b/vllm/model_executor/layers/fused_moe/experts/flashinfer_b12x_moe.py
index 6481434f2e78..38200d9d0905 100644
--- a/vllm/model_executor/layers/fused_moe/experts/flashinfer_b12x_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/flashinfer_b12x_moe.py
@@ -54,6 +54,11 @@ def __init__(
         self.out_dtype = moe_config.in_dtype
         self.num_local_experts = moe_config.num_local_experts
         self.ep_rank = moe_config.moe_parallel_config.ep_rank
+        # FC2 input scale tensor bound in process_weights_after_loading: the
+        # calibrated (now-zeroed) a2_gscale for static-quant checkpoints, or
+        # a synthesized uniform-1.0 tensor for W4A16 checkpoints that lack
+        # one. Holding it on the instance keeps apply() alloc-free.
+        self._fc2_input_scale: torch.Tensor | None = None
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         # Normalise block scales to absorb the per-expert weight global scale
@@ -86,6 +91,18 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         # its own per-block dynamic scale.
         if self.a2_gscale is not None:
             self.a2_gscale.fill_(1.0)
+            self._fc2_input_scale = self.a2_gscale
+        else:
+            # W4A16 NVFP4 checkpoints have no calibrated a2_gscale; b12x
+            # performs dynamic per-block FC2-input quantization, so a uniform
+            # 1.0 scale per expert is equivalent to the bake-in above for
+            # static-quant checkpoints. Allocate once here so apply() stays
+            # alloc-free.
+            self._fc2_input_scale = torch.ones(
+                self.num_local_experts,
+                device=layer.w13_weight.device,
+                dtype=torch.float32,
+            )
 
         # Precompute MMA-layout views of the weight scale factors once here
         # rather than recomputing on every forward pass.
@@ -131,7 +148,13 @@ def _supports_quant_scheme(
         weight_key: QuantKey | None,
         activation_key: QuantKey | None,
     ) -> bool:
-        return (weight_key, activation_key) == (kNvfp4Static, kNvfp4Dynamic)
+        # b12x performs in-kernel BF16->FP4 activation quant, so W4A16
+        # NVFP4 checkpoints (activation_key=None, e.g. mixed-precision
+        # compressed-tensors layouts) are runtime-compatible.
+        return (weight_key, activation_key) in (
+            (kNvfp4Static, kNvfp4Dynamic),
+            (kNvfp4Static, None),
+        )
 
     @staticmethod
     def _supports_activation(activation: MoEActivation) -> bool:
@@ -198,8 +221,8 @@ def apply(
         assert self.g1_alphas is not None and self.g2_alphas is not None, (
             "g1_alphas and g2_alphas must not be None for FlashInferB12xExperts"
         )
-        assert self.a2_gscale is not None, (
-            "a2_gscale must not be None for FlashInferB12xExperts"
+        assert self._fc2_input_scale is not None, (
+            "_fc2_input_scale must be set by process_weights_after_loading"
         )
 
         top_k = topk_ids.shape[1]
@@ -211,7 +234,7 @@ def apply(
             w1_weight=w1,
             w1_weight_sf=self.w1_sf_mma,
             w1_alpha=self.g1_alphas,
-            fc2_input_scale=self.a2_gscale,
+            fc2_input_scale=self._fc2_input_scale,
             w2_weight=w2,
             w2_weight_sf=self.w2_sf_mma,
             w2_alpha=self.g2_alphas,