diff --git a/python/sglang/srt/hardware_backend/npu/quantization/fp4_moe_methods.py b/python/sglang/srt/hardware_backend/npu/quantization/fp4_moe_methods.py
index e8df34a525e4..06c52f2fa058 100644
--- a/python/sglang/srt/hardware_backend/npu/quantization/fp4_moe_methods.py
+++ b/python/sglang/srt/hardware_backend/npu/quantization/fp4_moe_methods.py
@@ -171,10 +171,7 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
 
         if hasattr(layer, "dispatcher"):
             layer.dispatcher.set_quant_config(
-                {
-                    "normal_dispatcher_output_dtype": "bf16",
-                    "low_latency_dispatcher_output_dtype": "mxfp8",
-                }
+                dict(self.w13_kernel.DISPATCHER_QUANT_CONFIG)
             )
 
     def apply(self, layer: torch.nn.Module, dispatch_output: "DispatchOutput"):
diff --git a/python/sglang/srt/hardware_backend/npu/quantization/moe_methods.py b/python/sglang/srt/hardware_backend/npu/quantization/moe_methods.py
index 8c5534984ea9..0189d580b8f8 100644
--- a/python/sglang/srt/hardware_backend/npu/quantization/moe_methods.py
+++ b/python/sglang/srt/hardware_backend/npu/quantization/moe_methods.py
@@ -279,6 +279,15 @@ def _get_bias_args(
 class NPUW4A8MXFP4MoEMethod(_NPUMoEMethodBase):
     """ModelSlim W4A8 MoE with packed MXFP4 weights and MXFP8 activations."""
 
+    # Dispatch wire format required by the W4A8 MXFP GMMs: low-latency
+    # dispatch quantizes activations to MXFP8 (E8M0 block scales); normal
+    # dispatch stays BF16 because A5 MXFP8 normal dispatch is intranode-only,
+    # which also preserves multi-node prefill when ``deepep-mode=auto``.
+    DISPATCHER_QUANT_CONFIG = {
+        "normal_dispatcher_output_dtype": "bf16",
+        "low_latency_dispatcher_output_dtype": "mxfp8",
+    }
+
     def __init__(self, dynamic_quant_kwargs=_DEFAULT_DYNAMIC_QUANT):
         super().__init__(quant_config=None)
         self.dynamic_quant_kwargs = dynamic_quant_kwargs
@@ -308,17 +317,8 @@ def process_weights_after_loading(
             weight.data, weight_scale.data
         )
 
-        # A5 DeepEP low-latency dispatch quantizes the valid received rows to
-        # MXFP8 and returns the matching E8M0 block scales.  Keep normal mode
-        # in BF16 because A5 MXFP8 normal dispatch is intranode-only; this also
-        # preserves multi-node prefill when ``deepep-mode=auto``.
         if weight_prefix == "w13" and hasattr(layer, "dispatcher"):
-            layer.dispatcher.set_quant_config(
-                {
-                    "normal_dispatcher_output_dtype": "bf16",
-                    "low_latency_dispatcher_output_dtype": "mxfp8",
-                }
-            )
+            layer.dispatcher.set_quant_config(dict(self.DISPATCHER_QUANT_CONFIG))
 
     def apply(
         self,
diff --git a/python/sglang/srt/layers/quantization/fp8.py b/python/sglang/srt/layers/quantization/fp8.py
index 9d0c3e0116fa..f08fdbf9a94d 100644
--- a/python/sglang/srt/layers/quantization/fp8.py
+++ b/python/sglang/srt/layers/quantization/fp8.py
@@ -2671,8 +2671,17 @@ def process_weights_after_loading(self, layer: Module) -> None:
             self._prepare_hpc_ops_weights(layer)
 
         if hasattr(layer, "dispatcher"):
+            # Execution kernels may declare the dispatch wire format they
+            # need (DISPATCHER_QUANT_CONFIG, e.g. the NPU W4A8 MXFP GMMs
+            # consume MXFP8 activations); otherwise report the checkpoint
+            # dtype and let the dispatcher pick.
+            kernel_quant_config = getattr(
+                getattr(layer, "w13_kernel", None), "DISPATCHER_QUANT_CONFIG", None
+            )
             layer.dispatcher.set_quant_config(
-                {
+                kernel_quant_config
+                if kernel_quant_config is not None
+                else {
                     "weight_dtype": layer.w13_weight.dtype,
                     "use_mxfp8": self.use_mxfp8,
                 }