diff --git a/vllm/envs.py b/vllm/envs.py
index d7956a2adff6..45f3a4b9a4c3 100755
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -481,6 +481,17 @@ def get_vllm_port() -> Optional[int]:
     lambda: bool(int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"]))
     if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ else None,
 
+    # If set, vllm will force use Triton compressed tensor MOE kernel;
+    # otherwise it will use the cutlass based one.
+    "VLLM_TRITON_COMPRESSED_TENSORS_MOE_KERNEL":
+    lambda: bool(
+        int(os.getenv("VLLM_TRITON_COMPRESSED_TENSORS_MOE_KERNEL", "0"))),
+
+    # If set, vllm will force flashinfer to use tensor cores;
+    # otherwise will use heuristic based on model architecture.
+    "VLLM_FLASHINFER_FORCE_TENSOR_CORES":
+    lambda: bool(int(os.getenv("VLLM_FLASHINFER_FORCE_TENSOR_CORES", "0"))),
+
     # Pipeline stage partition strategy
     "VLLM_PP_LAYER_PARTITION":
     lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
index c2b884c058d3..87d611b67999 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
@@ -523,8 +523,10 @@ def __init__(
         # cutlass path
         self.is_fp8_w8a8_sm100 = quant_config._is_fp8_w8a8_sm100(
             self.weight_quant, self.input_quant)
-        self.use_cutlass = (quant_config._is_fp8_w8a8_sm90(
-            self.weight_quant, self.input_quant) or self.is_fp8_w8a8_sm100)
+        self.use_cutlass = ((quant_config._is_fp8_w8a8_sm90(
+            self.weight_quant, self.input_quant) or self.is_fp8_w8a8_sm100) and
+                            not envs.VLLM_TRITON_COMPRESSED_TENSORS_MOE_KERNEL)
+        self.fused_experts = None  # type: ignore[assignment]
         self.disable_expert_map = False
 
     def create_weights(self, layer: torch.nn.Module, num_experts: int,