diff --git a/atom/plugin/sglang/attention_backend/sgl_attention_mla.py b/atom/plugin/sglang/attention_backend/sgl_attention_mla.py
index a13ce5a6a..8b190acdb 100644
--- a/atom/plugin/sglang/attention_backend/sgl_attention_mla.py
+++ b/atom/plugin/sglang/attention_backend/sgl_attention_mla.py
@@ -236,6 +236,43 @@ def mla_absorbed_bmm(
     return torch.bmm(inp.transpose(0, 1), weight).transpose(0, 1)
 
 
+def mla_v_up_proj(
+    attn: DeepseekV2MLAAttention,
+    inp: torch.Tensor,
+    weight: torch.Tensor,
+    weight_scale: Optional[torch.Tensor],
+    weight_scale_k: Optional[torch.Tensor],
+    out_dim: int,
+) -> torch.Tensor:
+    """Project MLA decode output to a flat o_proj input."""
+    if _is_hip and (
+        (_use_aiter_gfx95 and weight.dtype == torch.float8_e4m3fn)
+        or (get_is_capture_mode() and weight.dtype == torch.float8_e4m3fnuz)
+    ):
+        x = inp.transpose(0, 1)
+        out = torch.empty(
+            (inp.shape[0], attn.num_local_heads * out_dim),
+            device=inp.device,
+            dtype=torch.bfloat16,
+        )
+        out_3d = out.view(inp.shape[0], attn.num_local_heads, out_dim)
+        batched_gemm_a8w8_a_per_token_group_prequant_w_per_batched_tensor_quant(
+            X=x,
+            WQ=weight.transpose(-1, -2),
+            w_scale=weight_scale,
+            group_size=128,
+            YQ=out_3d,
+            transpose_bm=True,
+            transpose_bm_in=False,
+            dtype=torch.bfloat16,
+        )
+        return out
+
+    return mla_absorbed_bmm(
+        attn, inp, weight, weight_scale, weight_scale_k, out_dim
+    ).flatten(1, 2)
+
+
 # Forward: prepare → core
 def forward_sgl_prepare(
     attn: DeepseekV2MLAAttention,
@@ -420,9 +457,9 @@ def forward_sgl_core(
     attn_output = attn_output.view(-1, attn.num_local_heads, attn.kv_lora_rank)
 
     # up-proj by w_vc
-    attn_bmm_output = mla_absorbed_bmm(
+    attn_bmm_output = mla_v_up_proj(
         attn, attn_output, attn.w_vc, attn.w_scale, attn.w_scale_v, attn.v_head_dim
-    ).flatten(1, 2)
+    )
 
     return attn.o_proj(attn_bmm_output)