diff --git a/vllm/envs.py b/vllm/envs.py
index b1030997f2..93a7c8069c 100644
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -49,6 +49,7 @@ if TYPE_CHECKING:
     VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
     VLLM_XLA_CHECK_RECOMPILATION: bool = False
     VLLM_FUSED_MOE_CHUNK_SIZE: int = 64 * 1024
+    VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
     VLLM_USE_RAY_SPMD_WORKER: bool = False
     VLLM_USE_RAY_COMPILED_DAG: bool = False
     VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: str = "auto"
@@ -535,6 +536,12 @@ environment_variables: dict[str, Callable[[], Any]] = {
     lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
     "VLLM_FUSED_MOE_CHUNK_SIZE":
     lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")),
+    # Control whether to use fused MoE activation chunking. Current chunking
+    # logic is incompatible with torch.compile and causes IMA. See issue
+    # https://github.com/vllm-project/vllm/issues/19631.
+    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING":
+    lambda: bool(
+        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))),
 
     # If set, vllm will skip the deprecation warnings.
     "VLLM_NO_DEPRECATION_WARNING":
diff --git a/vllm/model_executor/layers/fused_moe/modular_kernel.py b/vllm/model_executor/layers/fused_moe/modular_kernel.py
index ed3b6b8a1a..d25d70d3ef 100644
--- a/vllm/model_executor/layers/fused_moe/modular_kernel.py
+++ b/vllm/model_executor/layers/fused_moe/modular_kernel.py
@@ -225,6 +225,10 @@ class FusedMoEPermuteExpertsUnpermute(ABC):
         else:
             raise ValueError(f"Unsupported FusedMoe activation: {activation}")
 
+    def enable_chunking(self):
+        return envs.VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING and \
+          self.supports_chunking()
+
     @abstractmethod
     def apply(
         self,
@@ -400,7 +404,7 @@ class FusedMoEModularKernel(torch.nn.Module):
         else:
             _, M, N, K, top_k = _moe_problem_size(a1q, w1, w2, topk_ids)
 
-            if self.fused_experts.supports_chunking():
+            if self.fused_experts.enable_chunking():
                 CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
                 num_chunks = cdiv(M, CHUNK_SIZE)
             else: