diff --git a/benchmark/kernels/fused_moe_triton/benchmark_deepseekv3_moe_align_blocks.py b/benchmark/kernels/fused_moe_triton/benchmark_deepseekv3_moe_align_blocks.py
index e64f57d87..1d9504d0a 100644
--- a/benchmark/kernels/fused_moe_triton/benchmark_deepseekv3_moe_align_blocks.py
+++ b/benchmark/kernels/fused_moe_triton/benchmark_deepseekv3_moe_align_blocks.py
@@ -157,7 +157,7 @@ def calculate_diff(batch_size, seq_len):
     )
     sorted_ids_cuda.fill_(topk_ids.numel())
     max_num_m_blocks = max_num_tokens_padded // block_size
-    expert_ids_cuda = torch.empty(
+    expert_ids_cuda = torch.zeros(
         (max_num_m_blocks,), dtype=torch.int32, device=topk_ids.device
     )
     num_tokens_post_pad_cuda = torch.empty(
@@ -172,7 +172,7 @@ def calculate_diff(batch_size, seq_len):
 
     sorted_ids_triton = torch.empty_like(sorted_ids_cuda)
     sorted_ids_triton.fill_(topk_ids.numel())
-    expert_ids_triton = torch.empty_like(expert_ids_cuda)
+    expert_ids_triton = torch.zeros_like(expert_ids_cuda)
     num_tokens_post_pad_triton = torch.empty_like(num_tokens_post_pad_cuda)
 
     # compare the performance of cuda and triton implementation