diff --git a/vllm/models/glm5next/nvidia/mtp.py b/vllm/models/glm5next/nvidia/mtp.py
index ae1f7f5..35e1f41 100644
--- a/vllm/models/glm5next/nvidia/mtp.py
+++ b/vllm/models/glm5next/nvidia/mtp.py
@@ -110,10 +110,23 @@ class Glm5NextMultiTokenPredictorLayer(nn.Module):
         return hidden_states, hidden_states
 
 
+def _mtp_hf_config(vllm_config: VllmConfig):
+    """The MTP head's own config. With a standalone draft checkpoint
+    (``--speculative-config '{"model": ...}'``) the draft config carries
+    num_nextn_predict_layers / n_routed_experts, while vllm_config.model_config
+    is still the *target* (which may have num_nextn_predict_layers == 0, e.g. an
+    checkpoint that ships without an MTP layer). Fall back to the
+    target config for in-checkpoint MTP layers."""
+    spec = vllm_config.speculative_config
+    if spec is not None and spec.draft_model_config is not None:
+        return spec.draft_model_config.hf_config
+    return vllm_config.model_config.hf_config
+
+
 class Glm5NextMultiTokenPredictor(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
-        config = vllm_config.model_config.hf_config
+        config = _mtp_hf_config(vllm_config)
         self.mtp_start_layer_idx = config.num_hidden_layers
         self.num_mtp_layers = config.num_nextn_predict_layers
         self.layers = torch.nn.ModuleDict(
@@ -210,7 +223,7 @@ class Glm5NextMultiTokenPredictor(nn.Module):
 class Glm5NextMTP(nn.Module, DeepseekV2MixtureOfExperts):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
-        self.config = vllm_config.model_config.hf_config
+        self.config = _mtp_hf_config(vllm_config)
         self.quant_config = vllm_config.quant_config
         self.model = Glm5NextMultiTokenPredictor(
             vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")