diff --git a/python/sglang/srt/models/nemotron_h.py b/python/sglang/srt/models/nemotron_h.py
index b0b195750..655775447 100644
--- a/python/sglang/srt/models/nemotron_h.py
+++ b/python/sglang/srt/models/nemotron_h.py
@@ -972,6 +972,8 @@ class NemotronHForCausalLM(nn.Module):
                         continue
                     is_expert_weight = True
                     name_mapped = name.replace(weight_name, param_name)
+                    if name_mapped not in params_dict:
+                        continue
                     param = params_dict[name_mapped]
                     param.weight_loader(
                         param,
diff --git a/test/registered/unit/models/test_nemotron_h_weight_loading.py b/test/registered/unit/models/test_nemotron_h_weight_loading.py
new file mode 100644
index 000000000..8a5e30a4e
--- /dev/null
+++ b/test/registered/unit/models/test_nemotron_h_weight_loading.py
@@ -0,0 +1,87 @@
+"""
+Unit tests for NemotronHForCausalLM.load_weights.
+
+Regression test for Nemotron-H expert scale checkpoint tensors that map to
+parameters absent from the current runtime model.
+"""
+
+from sglang.test.ci.ci_register import register_cpu_ci
+
+register_cpu_ci(est_time=4, suite="stage-a-test-cpu")
+
+import unittest
+from types import SimpleNamespace
+
+import torch
+
+from sglang.srt.models.nemotron_h import NemotronHForCausalLM
+
+
+class _FakePPGroup:
+    is_first_rank = True
+    is_last_rank = True
+
+
+class _FakeParam:
+    def __init__(self):
+        self.loaded = None
+
+    def weight_loader(
+        self, param, loaded_weight, name, *, shard_id=None, expert_id=None
+    ):
+        self.loaded = (param, loaded_weight, name, shard_id, expert_id)
+
+
+class TestNemotronHWeightLoading(unittest.TestCase):
+    def _make_minimal_model(self, named_parameters=()):
+        model = object.__new__(NemotronHForCausalLM)
+        model.config = SimpleNamespace(n_routed_experts=2)
+        model.model = SimpleNamespace()
+        model.pp_group = _FakePPGroup()
+        model.remap_prefix = {}
+        model.remap_substr = {}
+        model.stacked_params_mapping = []
+        model.named_parameters = lambda: iter(named_parameters)
+        return model
+
+    def test_expert_input_scale_without_target_parameter_is_skipped(self):
+        """Expert scale weights absent from params_dict should not raise KeyError."""
+        model = self._make_minimal_model()
+        weights = [
+            (
+                "model.layers.1.mixer.experts.0.down_proj.input_scale",
+                torch.ones(1),
+            )
+        ]
+
+        model.load_weights(weights)
+
+    def test_expert_weight_with_target_parameter_is_loaded(self):
+        param = _FakeParam()
+        model = self._make_minimal_model(
+            [("model.layers.1.mixer.experts.w2_weight", param)]
+        )
+        loaded_weight = torch.ones(1)
+        weights = [
+            (
+                "model.layers.1.mixer.experts.0.down_proj.weight",
+                loaded_weight,
+            )
+        ]
+
+        model.load_weights(weights)
+
+        self.assertEqual(
+            param.loaded,
+            (
+                param,
+                loaded_weight,
+                "model.layers.1.mixer.experts.w2_weight",
+                "w2",
+                0,
+            ),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()