diff --git a/python/sglang/srt/layers/attention/vision.py b/python/sglang/srt/layers/attention/vision.py
index 526a85aa4..4970251ae 100644
--- a/python/sglang/srt/layers/attention/vision.py
+++ b/python/sglang/srt/layers/attention/vision.py
@@ -458,8 +458,19 @@ class VisionFlash3Attention(nn.Module):
         else:
             cu_seqlens = resolve_seqlens(cu_seqlens, bsz, seq_len, device=q.device)
             cu_seqlens = cu_seqlens.to(dtype=torch.int32).to(q.device)
-            seq_lens = cu_seqlens[1:] - cu_seqlens[:-1]
-            max_seqlen = seq_lens.max().item()
+            # Some vision encoders precompute this scalar once per encoder
+            # forward and share it across all of their attention blocks.  Use
+            # that value when available: deriving it here requires a
+            # GPU-to-host sync, so repeating it per block serializes the ViT
+            # launch stream for variable-size images.
+            max_seqlen = kwargs.get("max_seqlen")
+            if max_seqlen is None:
+                seq_lens = cu_seqlens[1:] - cu_seqlens[:-1]
+                max_seqlen = int(seq_lens.max().item())
+            elif isinstance(max_seqlen, torch.Tensor):
+                max_seqlen = int(max_seqlen.item())
+            else:
+                max_seqlen = int(max_seqlen)
 
             fa_kwargs = dict(
                 cu_seqlens_q=cu_seqlens,
diff --git a/python/sglang/srt/models/kimi_k25.py b/python/sglang/srt/models/kimi_k25.py
index 022d1aee2..60f0f5fd0 100644
--- a/python/sglang/srt/models/kimi_k25.py
+++ b/python/sglang/srt/models/kimi_k25.py
@@ -150,6 +150,7 @@ class MoonViTEncoderLayer(nn.Module):
             hidden_states,
             cu_seqlens=cu_seqlens,
             position_embeddings=rope_freqs_cis,
+            max_seqlen=max_seqlen,
         )
 
         hidden_states = residual + hidden_states
@@ -469,7 +470,10 @@ class MoonViT3dEncoder(nn.Module):
             )
         )
 
-        max_seqlen = lengths.max()
+        # FlashAttention needs a host integer.  Compute it once per MoonViT
+        # forward and pass it to every encoder block instead of synchronizing
+        # once per block inside the attention backend.
+        max_seqlen = int(lengths.max().item())
         cu_seqlens = lengths.to(hidden_states.device).cumsum(dim=0, dtype=torch.int32)
 
         for block in self.blocks:
diff --git a/test/registered/unit/layers/attention/test_vision_max_seqlen.py b/test/registered/unit/layers/attention/test_vision_max_seqlen.py
new file mode 100644
index 000000000..80e020e59
--- /dev/null
+++ b/test/registered/unit/layers/attention/test_vision_max_seqlen.py
@@ -0,0 +1,79 @@
+import sys
+
+import torch
+from torch import nn
+
+from sglang.srt.layers.attention import vision
+from sglang.srt.models.kimi_k25 import MoonViTEncoderLayer
+from sglang.test.ci.ci_register import register_cpu_ci
+
+register_cpu_ci(est_time=1, suite="base-a-test-cpu")
+
+
+def test_vision_flash3_uses_precomputed_max_seqlen(monkeypatch):
+    """A vision encoder can provide one host max-seqlen for all its blocks."""
+
+    recorded = {}
+
+    def fake_flash_attn(q, k, v, **kwargs):
+        recorded.update(kwargs)
+        return q
+
+    monkeypatch.setattr(vision, "_is_cuda", True)
+    # This symbol is imported only on CUDA/MUSA hosts; inject the stub on the
+    # CPU CI path too so the backend-selection behavior stays unit-testable.
+    monkeypatch.setattr(
+        vision, "flash_attn_varlen_func", fake_flash_attn, raising=False
+    )
+
+    attention = vision.VisionFlash3Attention(use_data_parallel=True)
+    q = torch.zeros(3, 1, 8)
+    cu_seqlens = torch.tensor([0, 1, 3], dtype=torch.int32)
+    output = attention(
+        q,
+        q,
+        q,
+        cu_seqlens=cu_seqlens,
+        bsz=1,
+        seq_len=3,
+        max_seqlen=17,
+    )
+
+    assert output is q
+    assert recorded["max_seqlen_q"] == 17
+    assert recorded["max_seqlen_k"] == 17
+
+
+def test_kimi_moonvit_forwards_one_precomputed_max_seqlen():
+    """MoonViT must share its encoder-level scalar with each attention block."""
+
+    recorded = {}
+
+    class CapturingAttention(nn.Module):
+        def forward(self, hidden_states, **kwargs):
+            recorded.update(kwargs)
+            return hidden_states
+
+    layer = MoonViTEncoderLayer.__new__(MoonViTEncoderLayer)
+    nn.Module.__init__(layer)
+    layer.norm0 = nn.Identity()
+    layer.norm1 = nn.Identity()
+    layer.attn = CapturingAttention()
+    layer.mlp = nn.Identity()
+
+    hidden_states = torch.ones(3, 4)
+    output = layer(
+        hidden_states,
+        cu_seqlens=torch.tensor([0, 3], dtype=torch.int32),
+        max_seqlen=19,
+        rope_freqs_cis=torch.ones(3, 2, dtype=torch.complex64),
+    )
+
+    assert torch.equal(output, hidden_states * 4)
+    assert recorded["max_seqlen"] == 19
+
+
+if __name__ == "__main__":
+    import pytest
+
+    sys.exit(pytest.main([__file__, "-v"]))