diff --git a/tests/kernels/test_moe.py b/tests/kernels/test_moe.py
index afb8b9f426a2..87989354db6a 100644
--- a/tests/kernels/test_moe.py
+++ b/tests/kernels/test_moe.py
@@ -3,6 +3,8 @@
 
 Run `pytest tests/kernels/test_moe.py`.
 """
+import unittest.mock as mock
+
 import pytest
 import torch
 from torch.nn import Parameter
@@ -40,6 +42,7 @@
 @pytest.mark.parametrize("topk", TOP_KS)
 @pytest.mark.parametrize("ep_size", EP_SIZE)
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
+@pytest.mark.parametrize("padding", [True, False])
 def test_fused_moe(
     m: int,
     n: int,
@@ -48,20 +51,20 @@ def test_fused_moe(
     topk: int,
     ep_size: int,
     dtype: torch.dtype,
+    padding: bool,
 ):
+    if padding:
+        padding_size = 128
+        envs.VLLM_MOE_PADDING = True
+    else:
+        padding_size = 0
+        envs.VLLM_MOE_PADDING = False
+
     a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
     w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
     w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
 
     score = torch.randn((m, e), device="cuda", dtype=dtype)
-
-    # Pad the input if use padding
-    if envs.VLLM_MOE_PADDING:
-        w1 = F.pad(w1, (0, 128), "constant", 0)
-        torch.cuda.empty_cache()
-        w2 = F.pad(w2, (0, 128), "constant", 0)
-        torch.cuda.empty_cache()
-
     if ep_size > 1:
         local_e = e // ep_size
         e_ids = torch.randint(0,
@@ -75,16 +78,7 @@ def test_fused_moe(
     else:
         e_map = None
 
-    triton_output = fused_moe(a,
-                              w1,
-                              w2,
-                              score,
-                              topk,
-                              global_num_experts=e,
-                              expert_map=e_map,
-                              renormalize=False)
     torch_output = torch_moe(a, w1, w2, score, topk, e_map)
-    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
     iterative_output = iterative_moe(a,
                                      w1,
                                      w2,
@@ -93,6 +87,26 @@ def test_fused_moe(
                                      global_num_experts=e,
                                      expert_map=e_map,
                                      renormalize=False)
+    # Pad the input if use padding
+    if envs.VLLM_MOE_PADDING:
+        w1 = F.pad(w1, (0, 128), "constant", 0)
+        torch.cuda.empty_cache()
+        w2 = F.pad(w2, (0, 128), "constant", 0)
+        torch.cuda.empty_cache()
+
+    with mock.patch(
+            'vllm.model_executor.layers.fused_moe.fused_moe.padding_size',
+            padding_size):
+        triton_output = fused_moe(a,
+                                  w1,
+                                  w2,
+                                  score,
+                                  topk,
+                                  global_num_experts=e,
+                                  expert_map=e_map,
+                                  renormalize=False)
+
+    torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
     torch.testing.assert_close(iterative_output,
                                torch_output,
                                atol=1e-2,
diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py
index 37be0fd9227f..7e3d05509bb5 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe.py
@@ -719,6 +719,8 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
             block_shape is not None and block_shape[1] > 0:
         assert B_scale is not None and B_scale.ndim == 3
         assert B_zp is None or B_zp.ndim == 3
+        assert padding_size == 0, "MoE padding is not supported " \
+              "with GPTQ/AWQ quantization"
 
         fused_moe_kernel_gptq_awq[grid](
             A,
@@ -770,7 +772,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
             expert_ids,
             num_tokens_post_padded,
             B.shape[1],
-            A.shape[1] - padding_size,
+            B.shape[2] - padding_size,
             EM,
             topk_ids.numel(),
             A.stride(0),