diff --git a/python/sglang/srt/models/mimo_v2.py b/python/sglang/srt/models/mimo_v2.py
index 41a95e4b6e..6f67bef0d5 100644
--- a/python/sglang/srt/models/mimo_v2.py
+++ b/python/sglang/srt/models/mimo_v2.py
@@ -1449,6 +1449,19 @@ class MiMoV2ForCausalLM(nn.Module, AudioEncoderMixin):
         # matching the draft's extract_context_feature (offset=1).
         self.model.layers_to_capture = [val + 1 for val in layer_ids]
 
+    _ROUTED_EXPERT_RE = re.compile(r"^model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.")
+
+    def _is_local_routed_expert(self, name: str) -> bool:
+        m = self._ROUTED_EXPERT_RE.match(name)
+        if m is None or self.model is None:
+            return True
+        layer_id = int(m.group(1))
+        if not 0 <= layer_id < len(self.model.layers):
+            return True
+        experts = getattr(getattr(self.model.layers[layer_id], "mlp", None), "experts", None)
+        mapper = getattr(experts, "_map_global_expert_id_to_local_expert_id", None)
+        return mapper is None or mapper(int(m.group(2))) >= 0
+
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
@@ -1599,6 +1612,11 @@ class MiMoV2ForCausalLM(nn.Module, AudioEncoderMixin):
                 continue
 
             if ".mlp.experts." in name and loaded_weight.dtype == torch.uint8:
+                # Skip experts stored on other EP ranks before touching the
+                # mmap: converting the UE8M0 scales of all 384 experts on every rank
+                # scales dominated load time on 8 x DGX Spark.
+                if not self._is_local_routed_expert(name):
+                    continue
                 if name.endswith(".weight_scale"):
                     name = name + "_inv"
                     loaded_weight = torch.exp2(loaded_weight.to(torch.float32) - 127.0)