diff --git a/python/sglang/srt/layers/attention/aiter_backend.py b/python/sglang/srt/layers/attention/aiter_backend.py
index 44c868eb994f..130209e753ea 100755
--- a/python/sglang/srt/layers/attention/aiter_backend.py
+++ b/python/sglang/srt/layers/attention/aiter_backend.py
@@ -186,8 +186,15 @@ def __init__(
                 )
 
         # aiter kernel related initialization
+        # Cap effective sequence length by actual KV cache capacity to avoid
+        # over-allocating the workspace buffer on memory-constrained GPUs.
+        # No single sequence can exceed max_total_num_tokens.
+        effective_max_seq_len = min(
+            self.max_context_len,
+            getattr(model_runner, "max_total_num_tokens", self.max_context_len),
+        )
         self.max_num_partitions = (
-            self.max_context_len + _AITER_PARTITION_SIZE_ROCM - 1
+            effective_max_seq_len + _AITER_PARTITION_SIZE_ROCM - 1
         ) // _AITER_PARTITION_SIZE_ROCM
 
         nbyes_per_qo_elem = torch.finfo(torch.float32).bits // 8
diff --git a/python/sglang/srt/layers/quantization/unquant.py b/python/sglang/srt/layers/quantization/unquant.py
index a1edcf5b4d44..0b5a982f2118 100644
--- a/python/sglang/srt/layers/quantization/unquant.py
+++ b/python/sglang/srt/layers/quantization/unquant.py
@@ -52,6 +52,7 @@
     from aiter import ActivationType
     from aiter.fused_moe import fused_moe
     from aiter.ops.shuffle import shuffle_weight
+    from aiter.tuned_gemm import tgemm
 
 if _is_npu:
     from sglang.srt.hardware_backend.npu.utils import npu_format_cast
@@ -150,6 +151,9 @@ def apply(
                 output = output.view(x_shapes[0], x_shapes[1], -1)
             return output
 
+        if _use_aiter and type(layer.weight.data) is torch.Tensor:
+            return tgemm.mm(x, layer.weight, bias, otype=x.dtype)
+
         return F.linear(x, layer.weight, bias)