diff --git a/tests/kernels/test_moe.py b/tests/kernels/test_moe.py
index 41aa6ec1601ee..4d9de33ff6a0d 100644
--- a/tests/kernels/test_moe.py
+++ b/tests/kernels/test_moe.py
@@ -116,15 +116,18 @@ def test_fused_moe_quant_int(m: int, n: int, k: int, e: int, topk: int,
         weight = weight.T
         qweight = qweight.T.contiguous().to(torch.uint8)
         scales = scales.T
-        qzeros = qzeros.T.contiguous().to(torch.uint8)
+        if has_zp:
+            qzeros = qzeros.T.contiguous().to(torch.uint8)
         if weight_bits == 4:
             qweight = qweight[:, 1::2] * 16 + qweight[:, ::2]
-            qzeros = qzeros[1::2, :] * 16 + qzeros[::2, :]
+            if has_zp:
+                qzeros = qzeros[1::2, :] * 16 + qzeros[::2, :]
 
         w_ref[expert_id] = weight
         w_qweight[expert_id] = qweight
         w_scales[expert_id] = scales
-        w_qzeros[expert_id] = qzeros
+        if has_zp:
+            w_qzeros[expert_id] = qzeros
 
     triton_output = fused_moe(a,
                               w1_qweight,
@@ -136,8 +139,8 @@ def test_fused_moe_quant_int(m: int, n: int, k: int, e: int, topk: int,
                               use_int8_w8a16=weight_bits == 8,
                               w1_scale=w1_scales,
                               w2_scale=w2_scales,
-                              w1_zp=w1_qzeros,
-                              w2_zp=w2_qzeros,
+                              w1_zp=w1_qzeros if has_zp else None,
+                              w2_zp=w2_qzeros if has_zp else None,
                               block_shape=[0, group_size])
     torch_output = torch_moe(a, w1_ref, w2_ref, score, topk)
     torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py
index 4b36262c0ca53..eda3ff96bc87b 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe.py
@@ -457,8 +457,7 @@ def moe_align_block_size(
                                       dtype=torch.int32,
                                       device=topk_ids.device)
     ops.moe_align_block_size(topk_ids, num_experts, block_size, sorted_ids,
-                             expert_ids, num_tokens_post_pad,
-                             num_experts >= 256)
+                             expert_ids, num_tokens_post_pad)
     return sorted_ids, expert_ids, num_tokens_post_pad