diff --git a/tests/compile/passes/test_rocm_aiter_qk_norm_rope_kvcache_fusion.py b/tests/compile/passes/test_rocm_aiter_qk_norm_rope_kvcache_fusion.py
index b5e354ddfd..900f90eb42 100644
--- a/tests/compile/passes/test_rocm_aiter_qk_norm_rope_kvcache_fusion.py
+++ b/tests/compile/passes/test_rocm_aiter_qk_norm_rope_kvcache_fusion.py
@@ -420,10 +420,9 @@ _FUSION_CONFIGS = [
 @pytest.mark.parametrize("use_shuffle_kv_layout", ["1", "0"])
 @pytest.mark.parametrize(
     "kv_stride_order",
-    [
-        pytest.param((0, 1, 2, 3, 4), id="block_first"),
-        pytest.param((1, 0, 2, 3, 4), id="kv_first"),
-    ],
+    # FA/unified use the 4D packed cache (num_blocks, num_kv_heads, block_size,
+    # 2*head_size); no separate K/V dim to permute.
+    [pytest.param((0, 1, 2, 3), id="packed_4d")],
 )
 @pytest.mark.parametrize("enable_aiter_triton_rope", [True, False])
 @pytest.mark.parametrize("block_size", [16, 32, 64])
@@ -458,6 +457,14 @@ def test_qk_norm_rope_kvcache_fusion(
         and use_shuffle_kv_layout == "1"
     ):
         pytest.skip("ROCM_AITER_UNIFIED_ATTN is NHD-only; shuffle env is ignored")
+    if (
+        attn_backend == AttentionBackendEnum.ROCM_AITER_FA
+        and use_shuffle_kv_layout == "1"
+    ):
+        pytest.skip(
+            "ROCM_AITER_FA gates the qk_norm+rope+kvcache fusion off under shuffle "
+            "layout (defers to reshape_and_cache_shuffle_triton), so nothing fuses"
+        )
     _run_qk_norm_rope_kvcache_fusion_test(
         attn_backend=attn_backend,
         enable_aiter_triton_rope=enable_aiter_triton_rope,
diff --git a/vllm/v1/attention/backends/rocm_aiter_fa.py b/vllm/v1/attention/backends/rocm_aiter_fa.py
index 5a204c7ed6..96dae0c266 100644
--- a/vllm/v1/attention/backends/rocm_aiter_fa.py
+++ b/vllm/v1/attention/backends/rocm_aiter_fa.py
@@ -1065,9 +1065,8 @@ class AiterFlashAttentionImpl(AttentionImpl):
         # Whenever making a change in this method, please benchmark the
         # performance to make sure it does not introduce any overhead.
         num_actual_tokens = attn_metadata.num_actual_tokens
-        # (B, H, N, 2*hs) -> ((B, N, H, hs), (B, N, H, hs))
-        key_cache, value_cache = kv_cache.transpose(1, 2).split(self.head_size, dim=-1)
 
+        key_cache, value_cache = self._split_kv_cache(kv_cache)
         if is_quantized_kv_cache(self.kv_cache_dtype):
             key_cache = key_cache.view(current_platform.fp8_dtype())
             value_cache = value_cache.view(current_platform.fp8_dtype())
@@ -1385,6 +1384,12 @@ class AiterFlashAttentionImpl(AttentionImpl):
 
         return output
 
+    def _split_kv_cache(
+        self, kv_cache: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # (B, H, N, 2*hs) -> ((B, N, H, hs), (B, N, H, hs))
+        return kv_cache.transpose(1, 2).split(self.head_size, dim=-1)
+
     def do_kv_cache_update(
         self,
         layer: AttentionLayer,
@@ -1393,8 +1398,7 @@ class AiterFlashAttentionImpl(AttentionImpl):
         kv_cache: torch.Tensor,
         slot_mapping: torch.Tensor,
     ):
-        # (B, H, N, 2*hs) -> ((B, N, H, hs), (B, N, H, hs))
-        key_cache, value_cache = kv_cache.transpose(1, 2).split(self.head_size, dim=-1)
+        key_cache, value_cache = self._split_kv_cache(kv_cache)
 
         # key and value may be None in the case of cross attention. They are
         # calculated once based on the output from the encoder and then cached
@@ -1449,7 +1453,12 @@ class AiterFlashAttentionImpl(AttentionImpl):
         )
 
     def fused_qk_norm_rope_kvcache_supported(self):
-        return rocm_aiter_ops.is_enabled()
+        # Only fuse when shuffle layout is off; the shuffle write path uses a
+        # dedicated cache update, mirroring fused_rope_kvcache_supported.
+        return (
+            rocm_aiter_ops.is_enabled()
+            and not rocm_aiter_ops.is_shuffle_kv_cache_enabled()
+        )
 
     def do_qk_norm_rope_kvcache_update(
         self,
@@ -1466,7 +1475,7 @@ class AiterFlashAttentionImpl(AttentionImpl):
         kv_cache: torch.Tensor,
         layer_slot_mapping: torch.Tensor,
     ):
-        key_cache, value_cache = kv_cache.unbind(1)
+        key_cache, value_cache = self._split_kv_cache(kv_cache)
         rocm_aiter_ops.do_qk_norm_rope_kvcache_update(
             qkv=qkv,
             q_weight=q_weight,
@@ -1502,7 +1511,7 @@ class AiterFlashAttentionImpl(AttentionImpl):
         layer_slot_mapping: torch.Tensor,
     ):
         # (B, H, N, 2*hs) -> ((B, N, H, hs), (B, N, H, hs))
-        key_cache, value_cache = kv_cache.transpose(1, 2).split(self.head_size, dim=-1)
+        key_cache, value_cache = self._split_kv_cache(kv_cache)
         flash_layout = True
 
         is_fp8_kv_cache = is_quantized_kv_cache(self.kv_cache_dtype)
diff --git a/vllm/v1/attention/backends/rocm_aiter_unified_attn.py b/vllm/v1/attention/backends/rocm_aiter_unified_attn.py
index 08cd869686..2ce0cf4585 100644
--- a/vllm/v1/attention/backends/rocm_aiter_unified_attn.py
+++ b/vllm/v1/attention/backends/rocm_aiter_unified_attn.py
@@ -329,9 +329,6 @@ class RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
         kv_cache: torch.Tensor,
         layer_slot_mapping: torch.Tensor,
     ):
-        # _split_kv_cache picks the unbind dim per layout (incl. the K/V-first
-        # encoder-decoder path). unified reads NHD, so never write the shuffle
-        # layout here.
         key_cache, value_cache = self._split_kv_cache(kv_cache)
         rocm_aiter_ops.do_qk_norm_rope_kvcache_update(
             qkv=qkv,