diff --git a/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py b/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
index 4482029c16a87..6d6a2e22bc5f3 100644
--- a/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
+++ b/vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from typing import Any, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 import torch
 
@@ -8,13 +8,16 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import extract_required_args
+from vllm.utils import has_triton_kernels
 
-if True:
+if has_triton_kernels():
     import triton_kernels.swiglu
-    from triton_kernels.matmul_ogs import (FnSpecs, FusedActivation,
-                                           PrecisionConfig, matmul_ogs)
+    from triton_kernels.matmul_ogs import FnSpecs, FusedActivation, matmul_ogs
     from triton_kernels.routing import routing
 
+if TYPE_CHECKING:
+    from triton_kernels.matmul_ogs import PrecisionConfig
+
 
 def triton_kernel_moe_forward(
     hidden_states: torch.Tensor,
@@ -33,8 +36,8 @@ def triton_kernel_moe_forward(
     w2_scale: Optional[torch.Tensor] = None,
     w1_bias: Optional[torch.Tensor] = None,
     w2_bias: Optional[torch.Tensor] = None,
-    w1_precision=None,  # PrecisionConfig or None
-    w2_precision=None,  # PrecisionConfig or None
+    w1_precision: Optional["PrecisionConfig"] = None,
+    w2_precision: Optional["PrecisionConfig"] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
     block_shape: Optional[list[int]] = None,
@@ -90,8 +93,8 @@ def triton_kernel_fused_experts(
     w2_scale: Optional[torch.Tensor] = None,
     w1_bias: Optional[torch.Tensor] = None,
     w2_bias: Optional[torch.Tensor] = None,
-    w1_precision=None,  # PrecisionConfig or None
-    w2_precision=None,  # PrecisionConfig or None
+    w1_precision: Optional["PrecisionConfig"] = None,
+    w2_precision: Optional["PrecisionConfig"] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
     block_shape: Optional[list[int]] = None,
@@ -141,8 +144,14 @@ def triton_kernel_fused_experts(
 
 class BatchedOAITritonExperts(mk.FusedMoEPermuteExpertsUnpermute):
 
-    def __init__(self, quant_config, max_num_tokens: int, num_dispatchers: int,
-                 w1_precision: PrecisionConfig, w2_precision: PrecisionConfig):
+    def __init__(
+        self,
+        quant_config,
+        max_num_tokens: int,
+        num_dispatchers: int,
+        w1_precision: "PrecisionConfig",
+        w2_precision: "PrecisionConfig",
+    ):
         super().__init__(quant_config)
         self.max_num_tokens = max_num_tokens
         self.num_dispatchers = num_dispatchers