diff --git a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_cute_dsl.py b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_cute_dsl.py
index 0b571a515109..b1d411b1649e 100644
--- a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_cute_dsl.py
+++ b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_cute_dsl.py
@@ -26,6 +26,7 @@
 from ...autotuner import (AutoTuner, ConstraintSpec, DynamicTensorSpec,
                           OptimizationProfile, TunableRunner, TuningConfig)
 from ...custom_ops.cute_dsl_custom_ops import GroupedGemmInputsHelper
+from ...cute_dsl_utils import IS_CUTLASS_DSL_AVAILABLE
 from ...model_config import ModelConfig
 from ...utils import (ActivationType, AuxStreamType, EventType,
                       Fp4QuantizedTensor,
@@ -39,6 +40,26 @@
 from .quantization import MoEWeightLoadingMode, NVFP4CuteDslFusedMoEMethod
 from .routing import BaseMoeRoutingMethod
 
+# These runners are defined inside cute_dsl_custom_ops' ``if
+# IS_CUTLASS_DSL_AVAILABLE:`` block, which has no else-branch, so importing them
+# unconditionally would break every importer of this file -- and create_moe
+# imports it eagerly under _torch.models, so that reaches all model startup
+# rather than just this backend. Guard the same way the sibling custom_ops
+# modules do, and leave the tuple empty when the DSL is absent: no CuteDSL
+# runner can be tuned in that case, so nothing can match it.
+_TILE_SIZE_CHECKED_RUNNERS: Tuple[type, ...] = ()
+if IS_CUTLASS_DSL_AVAILABLE:
+    from ...custom_ops.cute_dsl_custom_ops import (
+        Sm100BlockScaledContiguousGatherGroupedGemmActFusionRunner,
+        Sm100BlockScaledContiguousGroupedGemmFinalizeFusionRunner,
+        Sm100BlockScaledContiguousGroupedGemmRunner,
+        Sm100BlockScaledContiguousGroupedGemmSwigluFusionRunner)
+    _TILE_SIZE_CHECKED_RUNNERS = (
+        Sm100BlockScaledContiguousGroupedGemmRunner,
+        Sm100BlockScaledContiguousGroupedGemmFinalizeFusionRunner,
+        Sm100BlockScaledContiguousGroupedGemmSwigluFusionRunner,
+        Sm100BlockScaledContiguousGatherGroupedGemmActFusionRunner)
+
 
 @dataclass
 class NvFp4WeightView:
@@ -328,19 +349,9 @@ def runner_tactic_comb_checker(
         # eagerly under _torch.models, so that reaches all model startup rather
         # than just this backend. Reaching this line means a CuteDSL runner is
         # already being tuned, so the DSL is installed.
-        from ...custom_ops.cute_dsl_custom_ops import (
-            Sm100BlockScaledContiguousGatherGroupedGemmActFusionRunner,
-            Sm100BlockScaledContiguousGroupedGemmFinalizeFusionRunner,
-            Sm100BlockScaledContiguousGroupedGemmRunner,
-            Sm100BlockScaledContiguousGroupedGemmSwigluFusionRunner)
 
         for runner, tactic in comb:
-            if isinstance(
-                    runner,
-                (Sm100BlockScaledContiguousGroupedGemmRunner,
-                 Sm100BlockScaledContiguousGroupedGemmFinalizeFusionRunner,
-                 Sm100BlockScaledContiguousGroupedGemmSwigluFusionRunner,
-                 Sm100BlockScaledContiguousGatherGroupedGemmActFusionRunner)):
+            if isinstance(runner, _TILE_SIZE_CHECKED_RUNNERS):
                 mma_tiler_mn, *_ = tactic
                 if mma_tiler_mn[0] != tile_size:
                     return False
diff --git a/tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py b/tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py
index 8b1745638364..7952f4fa9fee 100644
--- a/tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py
+++ b/tests/unittest/_torch/thop/parallel/test_cute_dsl_moe.py
@@ -3,6 +3,7 @@
 from utils.util import check_accuracy
 
 from tensorrt_llm._torch.custom_ops.cute_dsl_custom_ops import GroupedGemmInputsHelper
+from tensorrt_llm._torch.cute_dsl_utils import IS_CUTLASS_DSL_AVAILABLE
 from tensorrt_llm._torch.modules.fused_moe.fused_moe_cute_dsl import cute_dsl_nvfp4_grouped_gemm_ref
 from tensorrt_llm._torch.modules.fused_moe.quantization import interleave_linear_and_gate
 from tensorrt_llm._torch.utils import (
@@ -413,6 +414,7 @@ def test_moe_gelu(dtype: str, num_tokens: int, top_k: int, tile_size: int):
     get_sm_version() not in (100, 103),
     reason="This test is only supported on SM 100 and SM 103 GPUs",
 )
+@pytest.mark.skipif(not IS_CUTLASS_DSL_AVAILABLE, reason="cutlass-dsl is not available")
 @pytest.mark.parametrize("tile_size", [128, 256])
 @pytest.mark.parametrize("ep_size", [1, 8, 32])
 @pytest.mark.parametrize("top_k", [1, 2, 8])
@@ -509,6 +511,7 @@ def test_nvfp4_grouped_gemm_blackwell(num_tokens: int, top_k: int, ep_size: int,
     get_sm_version() not in (100, 103),
     reason="This test is only supported on SM 100 and SM 103 GPUs",
 )
+@pytest.mark.skipif(not IS_CUTLASS_DSL_AVAILABLE, reason="cutlass-dsl is not available")
 @pytest.mark.parametrize("tile_size", [128, 256])
 @pytest.mark.parametrize("ep_size", [1, 8, 32])
 @pytest.mark.parametrize("top_k", [1, 2, 8])
@@ -610,6 +613,7 @@ def test_nvfp4_grouped_gemm_finalize_blackwell(
     get_sm_version() not in (100, 103),
     reason="This test is only supported on SM 100 and SM 103 GPUs",
 )
+@pytest.mark.skipif(not IS_CUTLASS_DSL_AVAILABLE, reason="cutlass-dsl is not available")
 @pytest.mark.parametrize("tile_size", [128, 256])
 @pytest.mark.parametrize("ep_size", [1, 8, 32])
 @pytest.mark.parametrize("top_k", [1, 2, 8])
@@ -735,6 +739,7 @@ def test_nvfp4_grouped_gemm_swiglu_blackwell(
     get_sm_version() not in (100, 103),
     reason="This test is only supported on SM 100 and SM 103 GPUs",
 )
+@pytest.mark.skipif(not IS_CUTLASS_DSL_AVAILABLE, reason="cutlass-dsl is not available")
 @pytest.mark.parametrize(
     "activation_type",
     [ActivationType.Swiglu, ActivationType.Relu2],