diff --git a/tests/models/inkling/test_moe_weight_layout.py b/tests/models/inkling/test_moe_weight_layout.py
index a7307e167a1..e15a573b845 100644
--- a/tests/models/inkling/test_moe_weight_layout.py
+++ b/tests/models/inkling/test_moe_weight_layout.py
@@ -187,6 +187,32 @@ def test_moe_loads_compressed_tensors_global_scale(
     assert loaded == [f"experts.routed_experts.{projection}_{scale_kind}_global_scale"]
 
 
+@pytest.mark.parametrize(("projection", "checkpoint_rows"), [("w13", 8), ("w2", 4)])
+def test_moe_loads_channelwise_scale_for_tp(
+    projection: str, checkpoint_rows: int
+) -> None:
+    param = torch.nn.Parameter(torch.empty(2, 4, 1))
+    experts = SimpleNamespace(
+        **{f"{projection}_weight_scale": param},
+        moe_config=SimpleNamespace(moe_parallel_config=SimpleNamespace(tp_rank=1)),
+    )
+    layer = SimpleNamespace(
+        experts=SimpleNamespace(routed_experts=experts),
+        _local_expert_slots=lambda: {0: 0, 2: 1},
+    )
+    checkpoint_scale = torch.arange(3 * checkpoint_rows).reshape(3, checkpoint_rows, 1)
+
+    loaded = moe.InklingMoE.load_expert_weight(
+        layer, f"experts.{projection}_weight_scale", checkpoint_scale
+    )
+
+    expected = checkpoint_scale[[0, 2]]
+    if projection == "w13":
+        expected = expected[:, 4:].reshape(2, 2, 2, 1).transpose(1, 2).flatten(1, 2)
+    torch.testing.assert_close(param, expected.float())
+    assert loaded == [f"experts.routed_experts.{projection}_weight_scale"]
+
+
 def test_sink_down_projection_is_packed_during_load(monkeypatch) -> None:
     monkeypatch.setattr(moe, "get_tensor_model_parallel_world_size", lambda: 2)
     monkeypatch.setattr(moe, "get_tensor_model_parallel_rank", lambda: 1)
diff --git a/vllm/models/inkling/nvidia/moe.py b/vllm/models/inkling/nvidia/moe.py
index 32f7489a4c4..9ff3acaa14d 100644
--- a/vllm/models/inkling/nvidia/moe.py
+++ b/vllm/models/inkling/nvidia/moe.py
@@ -589,6 +589,9 @@ class InklingMoE(nn.Module):
             param.data[lids] = vals.reshape(len(gids), *param.shape[1:]).to(
                 param.device
             )
+        elif key == "w2_weight_scale" and weight.shape[-1] == 1:
+            # Per-output-channel scales are replicated across TP ranks.
+            param.data[lids] = weight[gids].to(device=param.device, dtype=param.dtype)
         elif key.startswith("w13"):
             # Checkpoint w13 rows are interleaved [g0, u0, g1, u1, ...]; the
             # fused param layout is [w1(gate); w3(up)]. The TP-local rows form