diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py
index b434780e19a2..7b8919859985 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe.py
@@ -629,28 +629,32 @@ def invoke_fused_moe_wna16_triton_kernel(
     EM = sorted_token_ids.size(0)
     if A.size(0) < config["BLOCK_SIZE_M"]:
         # optimize for small batch_size.
-        # We assume that top_ids of each token is unique,
-        # so num_valid_experts <= batch_size <= BLOCK_SIZE_M,
-        # and we can skip some invalid blocks.
         EM = min(sorted_token_ids.size(0), A.size(0) * top_k * config["BLOCK_SIZE_M"])
+
+    config = config.copy()
+
+    block_config = get_moe_wna16_block_config(
+        config=config,
+        use_moe_wna16_cuda=False,
+        num_valid_tokens=num_tokens,
+        size_k=A.size(1),
+        size_n=B.size(1),
+        num_experts=B.size(1),
+        group_size=block_shape[1],
+        real_top_k=top_k,
+        block_size_m=config["BLOCK_SIZE_M"],
+    )
+    config.update(block_config)
+
+    if "BLOCK_SIZE_N" not in config:
+        config["BLOCK_SIZE_N"] = 64
+    if "BLOCK_SIZE_K" not in config:
+        config["BLOCK_SIZE_K"] = 32
+
     grid = lambda META: (
         triton.cdiv(EM, META["BLOCK_SIZE_M"])
         * triton.cdiv(B.size(1), META["BLOCK_SIZE_N"]),
     )
-    config = config.copy()
-    config.update(
-        get_moe_wna16_block_config(
-            config=config,
-            use_moe_wna16_cuda=False,
-            num_valid_tokens=num_tokens,
-            size_k=A.size(1),
-            size_n=B.size(1),
-            num_experts=B.size(1),
-            group_size=block_shape[1],
-            real_top_k=top_k,
-            block_size_m=config["BLOCK_SIZE_M"],
-        )
-    )
 
     fused_moe_kernel_gptq_awq[grid](
         A,
@@ -2373,12 +2377,13 @@ def apply(
             topk_ids, config["BLOCK_SIZE_M"], global_num_experts, expert_map
         )
 
-        invoke_fused_moe_triton_kernel(
+        dispatch_fused_moe_kernel(
             hidden_states,
             w1,
             intermediate_cache1,
             a1q_scale,
             self.w1_scale,
+            getattr(self, "w1_zp", None),
             None,  # topk_weights
             sorted_token_ids,
             expert_ids,
@@ -2410,12 +2415,13 @@ def apply(
             self.block_shape,
         )
 
-        invoke_fused_moe_triton_kernel(
+        dispatch_fused_moe_kernel(
             qintermediate_cache2,
             w2,
             intermediate_cache3,
             a2q_scale,
             self.w2_scale,
+            getattr(self, "w2_zp", None),
             topk_weights,
             sorted_token_ids,
             expert_ids,