diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py
index 023132acfed3..3c540df7d684 100644
--- a/vllm/model_executor/layers/fused_moe/layer.py
+++ b/vllm/model_executor/layers/fused_moe/layer.py
@@ -324,6 +324,7 @@ def __init__(
         expert_mapping: list[tuple[str, str, int, str]] | None = None,
         n_shared_experts: int | None = None,
         routing_method_type: int | None = None,
+        is_weights_interleaved: bool = False,
     ):
         super().__init__()
 
@@ -363,6 +364,8 @@ def __init__(
         )
         dp_size_ = dp_size if dp_size is not None else get_dp_group().world_size
 
+        self.is_weights_interleaved = is_weights_interleaved
+
         self.is_sequence_parallel = is_sequence_parallel
         self.sp_size = tp_size_ if is_sequence_parallel else 1
 
diff --git a/vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py b/vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py
index ce56887f1c26..4884e058f4ec 100644
--- a/vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py
+++ b/vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py
@@ -262,6 +262,19 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
                 else:
                     layer.cpu_fused_moe = cpu_fused_moe.IPEXFusedMOE(layer)
             else:
+                if getattr(layer, "is_weights_interleaved", False):
+                    from vllm.model_executor.layers.fused_moe.utils import (
+                        reorder_gate_up_to_halves,
+                    )
+
+                    layer.w13_weight.copy_(
+                        reorder_gate_up_to_halves(layer.w13_weight, axis=1)
+                    )
+                    if hasattr(layer, "w13_bias"):
+                        layer.w13_bias.copy_(
+                            reorder_gate_up_to_halves(layer.w13_bias, axis=-1)
+                        )
+                    layer.is_weights_interleaved = False
                 layer.cpu_fused_moe = cpu_fused_moe.CPUFusedMOE(layer)
 
     def apply(
diff --git a/vllm/model_executor/layers/fused_moe/utils.py b/vllm/model_executor/layers/fused_moe/utils.py
index 1f946d67a8f5..e919fc331be6 100644
--- a/vllm/model_executor/layers/fused_moe/utils.py
+++ b/vllm/model_executor/layers/fused_moe/utils.py
@@ -330,3 +330,23 @@ def activation_without_mul(activation: str) -> str:
 @functools.cache
 def disable_inplace() -> bool:
     return is_torch_equal_or_newer("2.9")
+
+
+def reorder_gate_up_to_halves(t: torch.Tensor, axis: int) -> torch.Tensor:
+    """
+    Treat dimension `axis` as interleaved [g0,u0,g1,u1,...] and reorder to
+    [g..., u...]. Always returns contiguous.
+    """
+    if axis < 0:
+        axis += t.ndim
+    size = t.shape[axis]
+    if size % 2 != 0:
+        return t.contiguous()
+    moved = axis != t.ndim - 1
+    if moved:
+        t = t.movedim(axis, -1)
+    shape = t.shape
+    t = t.reshape(shape[:-1] + (shape[-1] // 2, 2))
+    t = torch.cat([t[..., 0], t[..., 1]], dim=-1)
+    t = t.movedim(-1, axis).contiguous() if moved else t.contiguous()
+    return t
diff --git a/vllm/model_executor/models/gpt_oss.py b/vllm/model_executor/models/gpt_oss.py
index 7df3b087ccb8..715a0501d958 100644
--- a/vllm/model_executor/models/gpt_oss.py
+++ b/vllm/model_executor/models/gpt_oss.py
@@ -173,6 +173,7 @@ def __init__(
             has_bias=True,
             activation="swigluoai",
             is_sequence_parallel=self.is_sequence_parallel,
+            is_weights_interleaved=True,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor: