diff --git a/python/sglang/srt/layers/moe/fused_moe_triton/fused_moe_triton_kernels.py b/python/sglang/srt/layers/moe/fused_moe_triton/fused_moe_triton_kernels.py
index 8ce947d43a96..797332fef5dc 100644
--- a/python/sglang/srt/layers/moe/fused_moe_triton/fused_moe_triton_kernels.py
+++ b/python/sglang/srt/layers/moe/fused_moe_triton/fused_moe_triton_kernels.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import functools
 import os
 from typing import Any, Dict, List, Optional
 
@@ -20,9 +21,11 @@
 from sglang.srt.utils import (
     cpu_has_amx_support,
     get_bool_env_var,
+    get_device_name,
     is_cpu,
     is_cuda,
     is_hip,
+    is_sm90_supported,
 )
 
 try:
@@ -52,6 +55,24 @@ def support_tensor_descriptor():
     return _support_tensor_descriptor
 
 
+# In theory, swap_ab should benefit all SM90 GPUs.
+# However, since it has only been verified on H20 (not H100/H200),
+# it is currently enabled only on H20.
+@functools.lru_cache(maxsize=8)
+def should_enable_swap_ab(
+    BLOCK_SIZE_M: int,
+    BLOCK_SIZE_N: int,
+) -> bool:
+    device_name = get_device_name()
+    is_h20_device = device_name and "H20" in device_name and "H200" not in device_name
+    return (
+        is_h20_device
+        and is_sm90_supported()
+        and BLOCK_SIZE_M < 64
+        and BLOCK_SIZE_N >= 64
+    )
+
+
 @triton.jit
 def write_zeros_to_output(
     c_ptr,
@@ -360,6 +381,7 @@ def fused_moe_kernel(
     even_Ks: tl.constexpr,
     c_sorted: tl.constexpr,
     filter_expert: tl.constexpr,
+    swap_ab: tl.constexpr,
 ):
     """
     Implements the fused computation for a Mixture of Experts (MOE) using
@@ -498,7 +520,10 @@ def fused_moe_kernel(
     # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
     # of fp32 values for higher accuracy.
     # `accumulator` will be converted back to fp16 after the loop.
-    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    if swap_ab:
+        accumulator = tl.zeros((BLOCK_SIZE_N, BLOCK_SIZE_M), dtype=tl.float32)
+    else:
+        accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
 
     for k_start in range(0, K, BLOCK_SIZE_K):
         # Load the next block of A and B, generate a mask by checking the
@@ -539,12 +564,17 @@ def fused_moe_kernel(
                     a_scale_ptrs + offs_ks * stride_ask, mask=token_mask, other=0.0
                 )
                 b_scale = tl.load(b_scale_ptrs + offs_ks * stride_bsk)
+                if swap_ab:
+                    a, b = tl.trans(b, (1, 0)), tl.trans(a, (1, 0))
+                    a_scale, b_scale = b_scale, a_scale
                 if BLOCK_SIZE_N > group_n:
                     accumulator += tl.dot(a, b) * a_scale[:, None] * b_scale[None, :]
                 else:
                     accumulator += tl.dot(a, b) * (a_scale[:, None] * b_scale)
             else:
                 if use_fp8_w8a8:
+                    if swap_ab:
+                        a, b = tl.trans(b, (1, 0)), tl.trans(a, (1, 0))
                     accumulator = tl.dot(a, b, acc=accumulator)
                 else:
                     accumulator += tl.dot(a, b)
@@ -556,6 +586,9 @@ def fused_moe_kernel(
         if b_desc is None:
             b_ptrs += BLOCK_SIZE_K * stride_bk
 
+    if swap_ab:
+        accumulator = tl.trans(accumulator, (1, 0))
+
     if use_int8_w8a16:
         accumulator *= b_scale
     elif use_fp8_w8a8 or use_int8_w8a8:
@@ -615,6 +648,11 @@ def invoke_fused_moe_kernel(
     assert topk_weights.stride(1) == 1
     assert sorted_token_ids.stride(0) == 1
 
+    if use_fp8_w8a8:
+        swap_ab = should_enable_swap_ab(config["BLOCK_SIZE_M"], config["BLOCK_SIZE_N"])
+    else:
+        swap_ab = False
+
     padded_size = 0
     if use_fp8_w8a8:
         assert B_scale is not None
@@ -786,6 +824,7 @@ def alloc_fn(size: int, alignment: int, stream: Optional[int]):
             even_Ks=even_Ks,
             c_sorted=c_sorted,
             filter_expert=filter_expert,
+            swap_ab=swap_ab,
             **config,
         )