diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py
index 2620b679b6e..de906a861b7 100644
--- a/tests/quantization/test_compressed_tensors.py
+++ b/tests/quantization/test_compressed_tensors.py
@@ -10,6 +10,7 @@ from unittest.mock import Mock
 import pytest
 import torch
 from compressed_tensors.quantization import (
+    ActivationOrdering,
     QuantizationArgs,
     QuantizationStrategy,
     QuantizationType,
@@ -683,6 +684,35 @@ def test_compressed_tensors_mxfp8_moe_setup(vllm_runner):
         assert output
 
 
+@pytest.mark.parametrize(
+    "actorder,group_size,part,full,expected",
+    [
+        # actorder="group" with real grouping: must load full-K w2 scales and,
+        # when sharded (part != full), report is_k_full=False.
+        (ActivationOrdering.GROUP, 32, 64, 128, (True, 128, False)),
+        # actorder="group" but unsharded (part == full): full scales, k_full.
+        (ActivationOrdering.GROUP, 32, 128, 128, (True, 128, True)),
+        # actorder="group" with channel-wise (group_size == -1): no full load.
+        (ActivationOrdering.GROUP, -1, 64, 128, (False, 64, False)),
+        # "static"/"weight" reorder at quant time -> shard normally + k_full.
+        # Regression: static actorder under TP must keep is_k_full=True so the
+        # Marlin kernel never gets the invalid (group_size=16, is_k_full=0).
+        ("static", 32, 64, 128, (False, 64, True)),
+        ("weight", 32, 64, 128, (False, 64, True)),
+        (None, 32, 64, 128, (False, 64, True)),
+    ],
+)
+def test_wna16_marlin_moe_w2_scale_sharding(actorder, group_size, part, full, expected):
+    from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.compressed_tensors_moe_wna16_marlin import (  # noqa: E501
+        CompressedTensorsWNA16MarlinMoEMethod,
+    )
+
+    result = CompressedTensorsWNA16MarlinMoEMethod._w2_scale_sharding(
+        actorder, group_size, part, full
+    )
+    assert result == expected
+
+
 @pytest.mark.skipif(
     not current_platform.is_cuda() or not current_platform.has_device_capability(80),
     reason="MXFP4 requires ampere or newer",
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16.py
index 88303f189f5..cfeacc902f4 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16.py
@@ -97,6 +97,21 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
             num_groups_w2 = num_groups_w13 = 1
             self.group_size = -1
         else:
+            if hidden_size % self.group_size != 0:
+                raise ValueError(
+                    "CompressedTensors WNA16 MoE requires hidden_size "
+                    f"({hidden_size}) to be divisible by group_size "
+                    f"({self.group_size})."
+                )
+            if intermediate_size_per_partition % self.group_size != 0:
+                raise ValueError(
+                    "CompressedTensors WNA16 MoE with static group scales "
+                    "requires the MoE intermediate size per tensor-parallel "
+                    f"partition ({intermediate_size_per_partition}) to be "
+                    f"divisible by group_size ({self.group_size}). Scale "
+                    "groups would otherwise cross TP shard boundaries; use a "
+                    "compatible TP size or enable expert parallelism."
+                )
             num_groups_w2 = w2_scales_size // self.group_size
             num_groups_w13 = hidden_size // self.group_size
 
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16_marlin.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16_marlin.py
index 82734103917..0401a5b6e73 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16_marlin.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_wna16_marlin.py
@@ -181,6 +181,29 @@ class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod):
         backend_key = "Flashinfer" if is_flashinfer else "Marlin"
         return shape_map[weight_name][backend_key]
 
+    @staticmethod
+    def _w2_scale_sharding(
+        actorder,
+        group_size: int,
+        intermediate_size_per_partition: int,
+        intermediate_size_full: int,
+    ) -> tuple[bool, int, bool]:
+        """Decide how to shard w2 group scales across TP for WNA16 Marlin MoE.
+
+        Only ``actorder="group"`` permutes activations by ``g_idx`` at runtime
+        and therefore needs the full-K (unsharded) w2 scales plus ``is_k_full``.
+        ``actorder="weight"``/``"static"`` (and ``None``) reorder weights at
+        quantization time, so scales shard normally per TP rank.
+        """
+        load_full_w2 = (actorder == "group") and group_size != -1
+        w2_scales_size = (
+            intermediate_size_full if load_full_w2 else intermediate_size_per_partition
+        )
+        is_k_full = (actorder != "group") or (
+            intermediate_size_per_partition == intermediate_size_full
+        )
+        return load_full_w2, w2_scales_size, is_k_full
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -230,21 +253,36 @@ class CompressedTensorsWNA16MarlinMoEMethod(CompressedTensorsMoEMethod):
         layer.register_parameter("w2_weight_packed", w2_weight)
         set_weight_attrs(w2_weight, extra_weight_attrs)
 
-        # In the case where we have actorder/g_idx,
-        # we do not partition the w2 scales
-        load_full_w2 = self.actorder and self.group_size != -1
-        w2_scales_size = (
-            intermediate_size_full if load_full_w2 else intermediate_size_per_partition
-        )
-
-        self.is_k_full = (not self.actorder) or (
-            intermediate_size_per_partition == intermediate_size_full
+        load_full_w2, w2_scales_size, self.is_k_full = self._w2_scale_sharding(
+            self.actorder,
+            self.group_size,
+            intermediate_size_per_partition,
+            intermediate_size_full,
         )
 
         if self.strategy == "channel":
             num_groups_w2 = num_groups_w13 = 1
             self.group_size = -1
         else:
+            if hidden_size % self.group_size != 0:
+                raise ValueError(
+                    "CompressedTensors WNA16 Marlin MoE requires hidden_size "
+                    f"({hidden_size}) to be divisible by group_size "
+                    f"({self.group_size})."
+                )
+            if (
+                not load_full_w2
+                and intermediate_size_per_partition % self.group_size != 0
+            ):
+                raise ValueError(
+                    "CompressedTensors WNA16 Marlin MoE with static group "
+                    "scales requires the MoE intermediate size per "
+                    "tensor-parallel partition "
+                    f"({intermediate_size_per_partition}) to be divisible by "
+                    f"group_size ({self.group_size}). Scale groups would "
+                    "otherwise cross TP shard boundaries; use a compatible TP "
+                    "size or enable expert parallelism."
+                )
             num_groups_w2 = w2_scales_size // self.group_size
             num_groups_w13 = hidden_size // self.group_size