diff --git a/vllm/patches/deepseek_v4.py b/vllm/patches/deepseek_v4.py
index 634372f6..939a4737 100644
--- a/vllm/patches/deepseek_v4.py
+++ b/vllm/patches/deepseek_v4.py
@@ -221,7 +221,7 @@ class DeepseekV4MegaMoEExperts(nn.Module):
     It handles NVFP4 natively with full Blackwell pipeline overlap (TMA → MMA → Epilogue).
     This replaces the broken C++ CUTLASS kernel (see README.md for the full story).
     """
-    _cutedsl_runner: 'CuTeDSLMoERunner | None' = None
+    _cutedsl_compiled: bool = False
 
     # NVFP4 E2M1 lookup table (positive values, sign from bit 3)
     E2M1_LUT = [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0]
@@ -330,6 +330,7 @@ class DeepseekV4MegaMoEExperts(nn.Module):
         set_weight_attrs(self.w2_input_scale, weight_attrs)
 
         self._cutedsl_runner = None
+        self._cutedsl_compiled = False
 
         # Register in the static forward context so the custom-op wrapper
         # can look up this module by name from within a torch.compile graph.
@@ -505,23 +506,26 @@ class DeepseekV4MegaMoEExperts(nn.Module):
         self.w2_input_scale = None
 
         # Warm up the CuTeDSL kernel (JIT compiles MLIR→PTX on first call).
+        # Only compile once per process — the kernel is cached after that.
         # This takes ~1-2 min but prevents the vLLM RPC timeout (5 min) from
         # killing the engine when the first inference request triggers compilation.
         # Note: The MMA tiler is (128,128,256) — we need >= 128 tokens to fill
         # a tile. Using 128 tokens, 1 expert for the warmup.
-        print("  Compiling CuTeDSL NVFP4 MegaMoE kernel (one-time JIT, ~1-2 min)...", flush=True)
-        try:
-            device = self._cutedsl_runner.l1_fp4[0].device
-            dummy_hidden = torch.randn(128, self.hidden_size, dtype=torch.bfloat16, device=device)
-            dummy_ids = torch.zeros(128, 1, dtype=torch.int32, device=device)  # all to expert 0
-            dummy_weights = torch.ones(128, 1, dtype=torch.float32, device=device)
-            self._cutedsl_runner.run(dummy_hidden, dummy_weights, dummy_ids, expert_indices=[0])
-            print("  CuTeDSL NVFP4 MegaMoE kernel compiled ✓", flush=True)
-        except Exception as exc:
-            # CUDA illegal memory access corrupts the context — can't recover.
-            # Log the error clearly so the user knows to check the kernel.
-            print(f"  CuTeDSL warmup FAILED: {exc}", flush=True)
-            print("  The CUDA context may be corrupted. Check kernel alignment/tiling.", flush=True)
+        if not DeepseekV4MegaMoEExperts._cutedsl_compiled:
+            DeepseekV4MegaMoEExperts._cutedsl_compiled = True
+            print("  Compiling CuTeDSL NVFP4 MegaMoE kernel (one-time JIT, ~1-2 min)...", flush=True)
+            try:
+                device = self._cutedsl_runner.l1_fp4[0].device
+                dummy_hidden = torch.randn(128, self.hidden_size, dtype=torch.bfloat16, device=device)
+                dummy_ids = torch.zeros(128, 1, dtype=torch.int32, device=device)  # all to expert 0
+                dummy_weights = torch.ones(128, 1, dtype=torch.float32, device=device)
+                self._cutedsl_runner.run(dummy_hidden, dummy_weights, dummy_ids, expert_indices=[0])
+                print("  CuTeDSL NVFP4 MegaMoE kernel compiled ✓", flush=True)
+            except Exception as exc:
+                # CUDA illegal memory access corrupts the context — can't recover.
+                # Log the error clearly so the user knows to check the kernel.
+                print(f"  CuTeDSL warmup FAILED: {exc}", flush=True)
+                print("  The CUDA context may be corrupted. Check kernel alignment/tiling.", flush=True)
 
     def forward(
         self,