diff --git a/tests/kernels/moe/test_gpt_oss_triton_kernels.py b/tests/kernels/moe/test_gpt_oss_triton_kernels.py
index 4900949ad..630ea2e3f 100644
--- a/tests/kernels/moe/test_gpt_oss_triton_kernels.py
+++ b/tests/kernels/moe/test_gpt_oss_triton_kernels.py
@@ -26,9 +26,10 @@ from vllm.model_executor.layers.fused_moe.config import mxfp4_w4a16_moe_quant_co
 from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
     triton_kernel_moe_forward,
 )
-from vllm.model_executor.layers.utils import shuffle_weight
 from vllm.utils.math_utils import round_up
 
+from .utils import shuffle_weight
+
 
 def deshuffle(w: torch.Tensor):
     first = w[..., ::2]
diff --git a/tests/kernels/moe/test_modular_oai_triton_moe.py b/tests/kernels/moe/test_modular_oai_triton_moe.py
index cf9ff1863..99d96e970 100644
--- a/tests/kernels/moe/test_modular_oai_triton_moe.py
+++ b/tests/kernels/moe/test_modular_oai_triton_moe.py
@@ -33,11 +33,10 @@ from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEModularK
 from vllm.model_executor.layers.fused_moe.prepare_finalize import (
     MoEPrepareAndFinalizeNoEP,
 )
-from vllm.model_executor.layers.utils import shuffle_weight
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import set_random_seed
 
-from .utils import make_dummy_moe_config
+from .utils import make_dummy_moe_config, shuffle_weight
 
 MNK = [
     (1, 512, 384),
diff --git a/tests/kernels/moe/utils.py b/tests/kernels/moe/utils.py
index ef72b96be..e0a234111 100644
--- a/tests/kernels/moe/utils.py
+++ b/tests/kernels/moe/utils.py
@@ -33,6 +33,16 @@ from vllm.utils.deep_gemm import per_block_cast_to_fp8
 from vllm.utils.math_utils import round_up
 
 
+def shuffle_weight(w: torch.Tensor) -> torch.Tensor:
+    """Fold weights to adjacent locations for Triton MoE / SwiGLU kernel layout."""
+    shape = w.shape
+    n = shape[-1]
+    first = w[..., : n // 2]
+    second = w[..., n // 2 :]
+    stacked = torch.stack((first, second), dim=-1)
+    return stacked.reshape(shape)
+
+
 def make_dummy_moe_config(
     num_experts: int = 1,
     experts_per_token: int = 1,
diff --git a/vllm/model_executor/layers/utils.py b/vllm/model_executor/layers/utils.py
index 79d48a203..d1e35f583 100644
--- a/vllm/model_executor/layers/utils.py
+++ b/vllm/model_executor/layers/utils.py
@@ -31,27 +31,6 @@ def is_layer_moe_router_gate(prefix: str) -> bool:
     return prefix.rsplit(".", 1)[-1] in MOE_LAYER_ROUTER_GATE_SUFFIXES
 
 
-def shuffle_weight(w: torch.Tensor) -> torch.Tensor:
-    # Shuffle weight along the last dimension so that
-    # we folded the weights to adjance location
-    # Example:
-    # input:
-    #       [[1, 2, 3, 4, 5, 6],
-    #        [7, 8, 9, 10, 11, 12]]
-    # output:
-    #       [[1, 4, 2, 5, 3, 6],
-    #        [7, 10, 8, 11, 9, 12]]
-    # This will be used together with triton swiglu kernel
-    shape = w.shape
-    N = shape[-1]
-    first = w[..., : N // 2]
-    second = w[..., N // 2 :]
-
-    stacked = torch.stack((first, second), dim=-1)
-    w_shuffled = stacked.reshape(shape)
-    return w_shuffled
-
-
 def get_token_bin_counts_and_mask(
     tokens: torch.Tensor,
     vocab_size: int,