diff --git a/python/sglang/srt/layers/quantization/quark/quark.py b/python/sglang/srt/layers/quantization/quark/quark.py
index a9a8acb68..60db3f52b 100644
--- a/python/sglang/srt/layers/quantization/quark/quark.py
+++ b/python/sglang/srt/layers/quantization/quark/quark.py
@@ -322,6 +322,14 @@ class QuarkConfig(QuantizationConfig):
         self.kv_cache_config = kv_cache_config
         self.pack_method = pack_method
         self.exclude_layers = cast(list[str], self.quant_config.get("exclude", []))
+        # Both are consumed by _is_draft_layer(), which has to tell an appended
+        # MTP/NextN draft layer from a target-model one. "No draft stack" is
+        # spelled None as often as it is spelled absent -- ModelConfig defaults
+        # the same field to None -- so coerce rather than let range() raise.
+        self.num_hidden_layers = getattr(hf_config, "num_hidden_layers", None)
+        self.num_nextn_predict_layers = int(
+            getattr(hf_config, "num_nextn_predict_layers", 0) or 0
+        )
         self.is_prequantized = is_prequantized
         self.dequantization_config = dequantization_config
         # Load-as-is FP8 config for excluded layers of a mixed-precision source
@@ -555,6 +563,10 @@ class QuarkConfig(QuantizationConfig):
 
         return cls(
             quant_config=config,
+            # The requantization branch above takes hf_config off the same dict;
+            # the prequantized path needs it too, so _is_draft_layer() has the
+            # layer counts it compares against.
+            hf_config=config.get("hf_config"),
             kv_cache_group=kv_cache_group,
             kv_cache_config=kv_cache_config,
             pack_method=pack_method,
@@ -895,12 +907,34 @@ class QuarkConfig(QuantizationConfig):
     def get_scaled_act_names(self) -> List[str]:
         return []
 
+    def _is_draft_layer(self, layer: str) -> bool:
+        """Whether an excluded layer belongs to the MTP/NextN draft stack.
+
+        A draft layer is excluded from quantization in most checkpoints, and
+        says nothing about how the target model stores its own shared experts,
+        so it must not veto shared-expert fusion for the target model's layers.
+
+        Checkpoints spell it either as "mtp.*" or as extra entries appended to
+        the main decoder, model.layers.[num_hidden_layers ..
+        + num_nextn_predict_layers) -- the same range
+        get_spec_layer_idx_from_weight_name() walks in the MTP models.
+        """
+        if layer.startswith("mtp."):
+            return True
+        if self.num_hidden_layers is None:
+            return False
+        base = self.num_hidden_layers
+        return any(
+            layer.startswith(f"model.layers.{base + i}.")
+            for i in range(self.num_nextn_predict_layers)
+        )
+
     def can_fuse_shared_expert(self) -> bool:
         # Shared-expert body excluded from quant; the gate must not veto fusion.
         if any(
             "shared_expert" in layer
             and "shared_expert_gate" not in layer
-            and not layer.startswith("mtp.")
+            and not self._is_draft_layer(layer)
             for layer in self.exclude_layers
         ):
             return False