diff --git a/vllm/envs.py b/vllm/envs.py
index 7dcfabe3e044..53ce9ffe0a2d 100755
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -159,6 +159,7 @@ if TYPE_CHECKING:
     VLLM_USE_FLASHINFER_MOE_FP8: bool = False
     VLLM_USE_FLASHINFER_MOE_FP4: bool = False
     VLLM_FLASHINFER_MOE_BACKEND: Literal["throughput", "latency"] = "throughput"
+    VLLM_ALLOW_BATCHED_TRITON_FALLBACK: bool = False
     VLLM_XGRAMMAR_CACHE_MB: int = 0
     VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
     VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
@@ -1150,6 +1151,12 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16": lambda: bool(
         int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"))
     ),
+    # If set to 1, allow fallback to batched triton kernel when deepgemm
+    # is unavailable. By default (0), the system will crash if deepgemm
+    # is expected but not available.
+    "VLLM_ALLOW_BATCHED_TRITON_FALLBACK": lambda: bool(
+        int(os.getenv("VLLM_ALLOW_BATCHED_TRITON_FALLBACK", "0"))
+    ),
     # Control the cache sized used by the xgrammar compiler. The default
     # of 512 MB should be enough for roughly 1000 JSON schemas.
     # It can be changed with this variable if needed for some reason.
diff --git a/vllm/model_executor/layers/fused_moe/batched_triton_or_deep_gemm_moe.py b/vllm/model_executor/layers/fused_moe/batched_triton_or_deep_gemm_moe.py
index 1b1af351a449..04265ac83b01 100644
--- a/vllm/model_executor/layers/fused_moe/batched_triton_or_deep_gemm_moe.py
+++ b/vllm/model_executor/layers/fused_moe/batched_triton_or_deep_gemm_moe.py
@@ -3,6 +3,7 @@
 
 import torch
 
+import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
     BatchedDeepGemmExperts,
@@ -22,11 +23,8 @@ class BatchedTritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
     ):
         super().__init__(quant_config)
 
-        self.batched_triton_experts = BatchedTritonExperts(
-            max_num_tokens=max_num_tokens,
-            num_dispatchers=num_dispatchers,
-            quant_config=self.quant_config,
-        )
+        # Store the original request for deep gemm
+        deep_gemm_requested = allow_deep_gemm
 
         self.allow_deep_gemm = (
             allow_deep_gemm
@@ -44,6 +42,28 @@ class BatchedTritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
             else None
         )
 
+        # If deep gemm was requested but is not available (either due to
+        # unsupported configuration or missing dependencies), check if
+        # we should allow fallback to batched triton kernel
+        if deep_gemm_requested and self.batched_deep_gemm_experts is None:
+            if not envs.VLLM_ALLOW_BATCHED_TRITON_FALLBACK:
+                raise RuntimeError(
+                    "DeepGemm was requested but is not available. "
+                    "The batched triton kernel fallback is disabled by default. "
+                    "Set VLLM_ALLOW_BATCHED_TRITON_FALLBACK=1 to enable the fallback "
+                    "for debugging purposes."
+                )
+
+        self.batched_triton_experts = (
+            BatchedTritonExperts(
+                max_num_tokens=max_num_tokens,
+                num_dispatchers=num_dispatchers,
+                quant_config=self.quant_config,
+            )
+            if self.batched_deep_gemm_experts is None
+            else None
+        )
+
         assert (
             self.batched_deep_gemm_experts is not None
             or self.batched_triton_experts is not None