diff --git a/python/sglang/srt/environ.py b/python/sglang/srt/environ.py
index eb9455dc23c7..d2d1c0ee9f4f 100644
--- a/python/sglang/srt/environ.py
+++ b/python/sglang/srt/environ.py
@@ -499,6 +499,9 @@ class Envs:
     # decode without runtime permutes.
     SGLANG_AITER_KV_CACHE_LAYOUT = EnvStr("nhd")
     SGLANG_ROCM_FUSED_DECODE_MLA = EnvBool(False)
+    # Fuse the bf16 kv_b_proj GEMM + nope/v split + k_pe cat + fp8 cast into a
+    # single aiter Triton kernel (fused_gemm_a16w16_split_cat) on ROCm.
+    SGLANG_AITER_FUSED_KVB_SPLIT_CAT = EnvBool(False)
     SGLANG_ROCM_DISABLE_LINEARQUANT = EnvBool(False)
     SGLANG_MORI_NUM_MAX_DISPATCH_TOKENS_PER_RANK = EnvInt(4096)
     # Enable dual-stream MoE (shared experts vs routed experts) on the
diff --git a/python/sglang/srt/layers/attention/aiter_backend.py b/python/sglang/srt/layers/attention/aiter_backend.py
index 9d990a9ad35a..9bc63070c869 100755
--- a/python/sglang/srt/layers/attention/aiter_backend.py
+++ b/python/sglang/srt/layers/attention/aiter_backend.py
@@ -61,6 +61,7 @@
     )
 
 from sglang.srt.configs.model_config import AttentionArch
+from sglang.srt.environ import envs
 from sglang.srt.layers.attention.aiter_utils import (
     forward_decode_vectorized_5d,
     forward_extend_vectorized_5d,
@@ -84,6 +85,27 @@
     get_bool_env_var("SGLANG_AITER_FP8_PREFILL_ATTN", "True") and is_gfx95_supported()
 )
 
+# Opt-in (default off): fuse the bf16 kv_b_proj GEMM with its nope/v split,
+# k_pe cat, and fp8 cast into a single Triton kernel (bf16 analog of the MXFP4
+# fused_gemm_afp4wfp4_split_cat path). Only valid on gfx95 with bf16/fp16
+# kv_b_proj weights and fp8 prefill attention. Requires a recent aiter exposing
+# fused_gemm_a16w16_split_cat; falls back to the unfused path if not present.
+_has_fused_gemm_a16w16_split_cat = False
+try:
+    from aiter.ops.triton.gemm.fused.fused_gemm_a16w16_split_cat import (
+        fused_gemm_a16w16_split_cat,
+    )
+
+    _has_fused_gemm_a16w16_split_cat = True
+except ImportError:
+    pass
+
+_use_fused_kvb_split_cat = (
+    envs.SGLANG_AITER_FUSED_KVB_SPLIT_CAT.get()
+    and is_gfx95_supported()
+    and _has_fused_gemm_a16w16_split_cat
+)
+
 # Persist
 # fast_mode=True if _use_mla_ps_kernel else False
 # intra_batch_mode=False if _use_mla_ps_kernel else True
@@ -2028,6 +2050,25 @@ def forward_extend(
                                 fp8_dtype,
                             )
                         )[0]
+                    elif (
+                        _use_fp8_prefill_attn
+                        and _use_fused_kvb_split_cat
+                        and layer.kv_b_proj.weight.dtype
+                        in (torch.bfloat16, torch.float16)
+                    ):
+                        # BF16 weights + FP8 prefill: fuse the kv_b_proj GEMM,
+                        # nope/v split, and k_pe cat into a single kernel
+                        # (fused_gemm_a16w16_split_cat) that writes k and v
+                        # directly in FP8, avoiding separate split / cat /
+                        # float8_copy passes.
+                        k, v = fused_gemm_a16w16_split_cat(
+                            x=kvc.squeeze(1).contiguous(),
+                            w=layer.kv_b_proj.weight,
+                            y=k_pe.expand(-1, layer.tp_k_head_num, -1),
+                            S1=qk_nope_head_dim,
+                            S2=layer.v_head_dim,
+                            dtype=fp8_dtype,
+                        )
                     else:
                         kv = layer.kv_b_proj(kvc.contiguous())[0]
 
diff --git a/python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mha.py b/python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mha.py
index 729be49044eb..a9d3265dd402 100644
--- a/python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mha.py
+++ b/python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mha.py
@@ -39,12 +39,32 @@
 elif _is_musa:
     from sgl_kernel import concat_mla_k
 
+# Opt-in (default off): fuse the bf16 kv_b_proj GEMM with its nope/v split,
+# k_pe cat, and fp8 cast into a single Triton kernel (the bf16 analog of the
+# MXFP4 fused_gemm_afp4wfp4_split_cat path). Requires a recent aiter exposing
+# fused_gemm_a16w16_split_cat; falls back to the unfused path if not present.
+_has_fused_gemm_a16w16_split_cat = False
 if _use_aiter_gfx95:
     from aiter.ops.triton.fused_fp8_quant import fused_rms_fp8_group_quant
 
     from sglang.srt.layers.quantization.fp8_kernel import fp8_dtype
     from sglang.srt.layers.quantization.rocm_mxfp4_utils import fused_rms_mxfp4_quant
 
+    try:
+        from aiter.ops.triton.gemm.fused.fused_gemm_a16w16_split_cat import (
+            fused_gemm_a16w16_split_cat,
+        )
+
+        _has_fused_gemm_a16w16_split_cat = True
+    except ImportError:
+        pass
+
+_use_fused_kvb_split_cat = (
+    envs.SGLANG_AITER_FUSED_KVB_SPLIT_CAT.get()
+    and _use_aiter_gfx95
+    and _has_fused_gemm_a16w16_split_cat
+)
+
 
 def _resolve_attn_backend(forward_batch: ForwardBatch):
     backend = get_attn_backend()
@@ -289,6 +309,23 @@ def forward_normal_prepare(
                     fp8_dtype,
                 )
             )[0]
+        elif (
+            _use_fp8_prefill_attn
+            and _use_fused_kvb_split_cat
+            and self.kv_b_proj.weight.dtype in (torch.bfloat16, torch.float16)
+        ):
+            # BF16 weights + FP8 prefill: fuse the kv_b_proj GEMM, nope/v split,
+            # and k_pe cat into a single kernel (fused_gemm_a16w16_split_cat)
+            # that writes k and v directly in FP8, avoiding separate split / cat
+            # / float8_copy passes.
+            k, v = fused_gemm_a16w16_split_cat(
+                x=kv_a,
+                w=self.kv_b_proj.weight,
+                y=k_pe.expand(-1, self.num_local_heads, -1),
+                S1=self.qk_nope_head_dim,
+                S2=self.v_head_dim,
+                dtype=fp8_dtype,
+            )
         else:
             if _use_aiter_gfx95 and self.kv_b_proj.weight.dtype == torch.float8_e4m3fn:
                 kv = self.kv_b_proj(kv_a_quanted)[0]