diff --git a/vllm/nvfp4_cutedsl.py b/vllm/nvfp4_cutedsl.py
index 427599e6..e6b4567b 100644
--- a/vllm/nvfp4_cutedsl.py
+++ b/vllm/nvfp4_cutedsl.py
@@ -109,22 +109,7 @@ class CuTeDSLMoERunner:
         if self._l1_mat_b is not None:
             return
         
-        # Allocate token indices FIRST, before any CUDA JIT compilation
-        # (CuTeDSL's cute.compile can corrupt GPU memory state during JIT)
-        # Use .clone() to ensure a fresh allocation and cuda.synchronize() to
-        # ensure the kernel completes before any JIT compilation starts
-        if self._token_indices is None:
-            self._token_indices = torch.arange(
-                self.max_num_tokens, device=self.device, dtype=torch.int32
-            ).unsqueeze(1).expand(-1, self.top_k).contiguous().view(-1).clone()
-            torch.cuda.synchronize()
-        if self._expert_id_range is None:
-            self._expert_id_range = torch.arange(self.num_experts, device=self.device)
-        if self._expert_offsets_buf is None:
-            self._expert_offsets_buf = torch.zeros(
-                self.num_experts + 1, dtype=torch.int32, device=self.device
-            )
-        
+        # Stack and prepare weight tensors FIRST (triggers CuTeDSL JIT compilation)
         self._l1_mat_b = make_b_k_major(torch.stack(self.l1_fp4))
         self._l2_mat_b = make_b_k_major(torch.stack(self.l2_fp4))
         self._l1_scale_b = assemble_scales_3d_side(self.l1_sf)
@@ -137,6 +122,19 @@ class CuTeDSLMoERunner:
         self.l2_fp4 = None
         self.l2_sf = None
         self.l2_gs = None
+        
+        # Allocate buffers AFTER JIT compilation
+        # (CuTeDSL's cute.compile corrupts GPU memory during JIT;
+        #  tensors allocated before/during compilation may be zeroed)
+        self._token_indices = torch.arange(
+            self.max_num_tokens, dtype=torch.int32
+        ).unsqueeze(1).expand(-1, self.top_k).contiguous().view(-1).to(self.device)
+        self._expert_id_range = torch.arange(
+            self.num_experts, dtype=torch.int32
+        ).to(self.device)
+        self._expert_offsets_buf = torch.zeros(
+            self.num_experts + 1, dtype=torch.int32, device=self.device
+        )
         self._allocate_buffers()
 
     def prepare_weights_direct(self, l1_fp4, l1_sf, l1_gs, l2_fp4, l2_sf, l2_gs):