diff --git a/python/sglang/srt/environ.py b/python/sglang/srt/environ.py
index 1ab737a1fab0..c84c24e2b862 100644
--- a/python/sglang/srt/environ.py
+++ b/python/sglang/srt/environ.py
@@ -1070,6 +1070,9 @@ class Envs:
     # DeepGEMM Mega MoE
     # ===================================================================
     SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK = EnvInt(8192)
+    # Blackwell MegaMoE uses a whole-grid software barrier. Keep a small
+    # residency margin so every cluster can launch beside other streams.
+    SGLANG_OPT_DEEPGEMM_MEGA_MOE_RESERVED_SMS = EnvInt(2)
 
     # ===================================================================
     # Top-k kernels
diff --git a/python/sglang/srt/layers/moe/mega_moe.py b/python/sglang/srt/layers/moe/mega_moe.py
index 5709b93425c9..3485fd841a95 100644
--- a/python/sglang/srt/layers/moe/mega_moe.py
+++ b/python/sglang/srt/layers/moe/mega_moe.py
@@ -15,8 +15,9 @@
 
 from __future__ import annotations
 
+import functools
 import os
-from contextlib import nullcontext
+from contextlib import contextmanager, nullcontext
 from typing import TYPE_CHECKING, Optional
 
 import torch
@@ -44,6 +45,43 @@
 _MEGA_MOE_SYMM_BUFFER: dict = {}
 
 
+@functools.lru_cache(maxsize=1)
+def _mega_moe_max_num_sms() -> Optional[int]:
+    if _device_sm < 100:
+        # The SM90 MegaMoE implementation does not use the whole-grid clustered
+        # launch that needs a residency margin.
+        return None
+
+    # Physical count, not deep_gemm.get_num_sms(): two-batch overlap and the DSA
+    # indexer reconfigure that process-wide, so reserving on top would compound.
+    num_sms = torch.cuda.get_device_properties(device="cuda").multi_processor_count
+    reserved_num_sms = max(envs.SGLANG_OPT_DEEPGEMM_MEGA_MOE_RESERVED_SMS.get(), 0)
+    return max(2, num_sms - reserved_num_sms)
+
+
+@contextmanager
+def _configure_mega_moe_deep_gemm_num_sms(deep_gemm):
+    max_num_sms = _mega_moe_max_num_sms()
+    if max_num_sms is None:
+        yield
+        return
+
+    current_num_sms = deep_gemm.get_num_sms()
+    # Stay under an outer context's budget instead of claiming SMs back from it.
+    target_num_sms = min(max_num_sms, current_num_sms)
+    # Round down: the clustered launch needs an even CTA count.
+    target_num_sms -= target_num_sms % 2
+    if target_num_sms == current_num_sms:
+        yield
+        return
+
+    deep_gemm.set_num_sms(target_num_sms)
+    try:
+        yield
+    finally:
+        deep_gemm.set_num_sms(current_num_sms)
+
+
 def _get_mega_moe_symm_buffer(
     group,
     num_experts: int,
@@ -247,16 +285,17 @@ def _run_mega_routed(
         device=hidden_states.device,
     )
     swiglu_limit = getattr(moe.config, "swiglu_limit", None)
-    deep_gemm.fp8_fp4_mega_moe(
-        y,
-        moe.experts.mega_l1_weights,
-        moe.experts.mega_l2_weights,
-        buf,
-        recipe=(1, 1, 32),
-        activation="swiglu",
-        activation_clamp=swiglu_limit,
-        fast_math=True,
-    )
+    with _configure_mega_moe_deep_gemm_num_sms(deep_gemm):
+        deep_gemm.fp8_fp4_mega_moe(
+            y,
+            moe.experts.mega_l1_weights,
+            moe.experts.mega_l2_weights,
+            buf,
+            recipe=(1, 1, 32),
+            activation="swiglu",
+            activation_clamp=swiglu_limit,
+            fast_math=True,
+        )
     y = y[:num_tokens]
 
     if not moe.experts.should_fuse_routed_scaling_factor_in_topk:
diff --git a/python/sglang/srt/models/kimi_k3.py b/python/sglang/srt/models/kimi_k3.py
index c164b4c0a8bd..5d9997b11c7e 100644
--- a/python/sglang/srt/models/kimi_k3.py
+++ b/python/sglang/srt/models/kimi_k3.py
@@ -780,7 +780,10 @@ def _forward_mega_experts(
         from sglang.kernels.ops.attention.dsv4 import mega_moe_pre_dispatch
         from sglang.srt.distributed.parallel_state import get_moe_ep_group
         from sglang.srt.environ import envs
-        from sglang.srt.layers.moe.mega_moe import _get_mega_moe_symm_buffer
+        from sglang.srt.layers.moe.mega_moe import (
+            _configure_mega_moe_deep_gemm_num_sms,
+            _get_mega_moe_symm_buffer,
+        )
 
         # In SP-MoE mode (KimiK3DecoderLayer reduce-scatters the o_proj
         # output) the incoming rows are already this rank's token shard, so
@@ -833,15 +836,16 @@ def _forward_mega_experts(
             dtype=torch.bfloat16,
             device=routed_input.device,
         )
-        deep_gemm.fp8_fp4_mega_moe(
-            y,
-            self.experts.mega_l1_weights,
-            self.experts.mega_l2_weights,
-            buf,
-            recipe=(1, 1, 32),
-            activation="situ",
-            fast_math=True,
-        )
+        with _configure_mega_moe_deep_gemm_num_sms(deep_gemm):
+            deep_gemm.fp8_fp4_mega_moe(
+                y,
+                self.experts.mega_l1_weights,
+                self.experts.mega_l2_weights,
+                buf,
+                recipe=(1, 1, 32),
+                activation="situ",
+                fast_math=True,
+            )
         y = y[:num_tokens]
         if not self.experts.should_fuse_routed_scaling_factor_in_topk:
             if (