diff --git a/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py b/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
index 81e0ea75..1d5c04ff 100644
--- a/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
+++ b/src/nvfp4_megamoe_kernel/nvfp4_mega_moe.py
@@ -279,15 +279,27 @@ def nvfp4_mega_moe_full(
     topk_ids = symm_buffer.topk_idx[:num_tokens]
     topk_weights = symm_buffer.topk_weights[:num_tokens]
 
+    # Convert global expert IDs to local expert IDs.
+    # vLLM's symm_buffer stores global IDs (0..383) but our weight tensors
+    # are indexed by local ID (0..47). Each rank handles a contiguous chunk:
+    # rank r gets experts [r*E_per_rank, (r+1)*E_per_rank).
+    # We derive the start index from the first global ID that maps to local 0.
+    num_experts_per_rank = l1_w.shape[0]
+    # Find experts_start_idx: the minimum global ID that this rank handles.
+    # All topk_ids in the buffer should fall within this rank's range.
+    experts_start_idx = (topk_ids.min().item() // num_experts_per_rank) * num_experts_per_rank
+    topk_ids_local = topk_ids - experts_start_idx
+
     if MEGA_MOE_DEBUG:
         print(f"[nvfp4_mega_moe_full] x_fp4={x_fp4.shape} x_sf={x_sf.shape} "
-              f"topk_ids={topk_ids.shape} l1_w={l1_w.shape} l2_w={l2_w.shape}")
+              f"topk_ids={topk_ids.shape} topk_ids range: {topk_ids.min().item()}-{topk_ids.max().item()} "
+              f"local: {topk_ids_local.min().item()}-{topk_ids_local.max().item()} "
+              f"l1_w={l1_w.shape} l2_w={l2_w.shape}")
 
     # Step 2: L1 GEMM (native NVFP4 block-scaled MMA)
-    num_experts_per_rank = l1_w.shape[0]
     l1_output = nvfp4_mega_moe_l1(
         x_fp4, x_sf, l1_w, l1_sf,
-        topk_ids, topk_weights, num_experts_per_rank,
+        topk_ids_local, topk_weights, num_experts_per_rank,
     )
 
     # Step 3: SiLU + Mul
@@ -302,7 +314,7 @@ def nvfp4_mega_moe_full(
     # Step 5: L2 GEMM (native NVFP4 block-scaled MMA)
     l2_output = nvfp4_mega_moe_l2(
         l1_fp4, l1_sf_out, l2_w, l2_sf,
-        topk_ids, topk_weights, num_experts_per_rank,
+        topk_ids_local, topk_weights, num_experts_per_rank,
     )
 
     # Step 6: Write to output (caller handles cross-rank all-reduce)