diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py
index 44797874a4..a268ca1974 100644
--- a/vllm/model_executor/models/deepseek_v2.py
+++ b/vllm/model_executor/models/deepseek_v2.py
@@ -612,6 +612,7 @@ class Indexer(nn.Module):
         cache_config: CacheConfig | None,
         topk_indices_buffer: torch.Tensor | None,
         prefix: str = "",
+        is_inplace_rope: bool = False,
     ):
         super().__init__()
         self.vllm_config = vllm_config
@@ -673,15 +674,21 @@ class Indexer(nn.Module):
             self.topk_indices_buffer,
         )
 
+        self.is_inplace_rope = is_inplace_rope
+
     def forward(
         self, hidden_states: torch.Tensor, qr: torch.Tensor, positions, rotary_emb
     ) -> torch.Tensor:
         q, _ = self.wq_b(qr)
         q = q.view(-1, self.n_head, self.head_dim)
 
-        if current_platform.is_rocm():
+        if current_platform.is_rocm() and self.is_inplace_rope:
             # This path should works on all platform, will remove extra
             # branches in the future
+            # This fast path relies on rotary_emb mutating q and k inplace.
+            # On ROCm, this is only valid for kernels used as custom ops.
+            # In pytorch-native rope for inductor fusion, rotated q/k tensors
+            # are not mutated inplace but returned as new tensors.
             # Fused wk + weights_proj: one GEMM, then split
             kw, _ = self.wk_weights_proj(hidden_states)
             k = kw[:, : self.head_dim]
@@ -1002,6 +1009,7 @@ class DeepseekV2MLAAttention(nn.Module):
                 cache_config,
                 topk_indices_buffer,
                 f"{prefix}.indexer",
+                is_inplace_rope=self.indexer_rope_emb.enabled(),
             )
 
             # Enable IndexCache for DeepSeek models to reduce redundant top-k
diff --git a/vllm/v1/attention/ops/rocm_aiter_mla_sparse.py b/vllm/v1/attention/ops/rocm_aiter_mla_sparse.py
index 80731296fc..7ddcc49344 100644
--- a/vllm/v1/attention/ops/rocm_aiter_mla_sparse.py
+++ b/vllm/v1/attention/ops/rocm_aiter_mla_sparse.py
@@ -107,6 +107,7 @@ def indexer_k_quant_and_cache_triton(
     kv_cache_value = kv_cache[:, : block_size * head_dim].view(fp8_dtype)
     kv_cache_scale = kv_cache[:, block_size * head_dim :].view(torch.float32)
     head_tile_size = head_tile_size // kv_cache.element_size()
+    layout = "NORMAL" if block_size == 1 else "SHUFFLE"
     grid = (num_tokens,)
     _indexer_k_quant_and_cache_kernel[grid](
         k,
@@ -118,7 +119,7 @@ def indexer_k_quant_and_cache_triton(
         block_size,
         num_tokens,
         head_dim,
-        "SHUFFLE",
+        layout,
         block_tile_size,
         head_tile_size,
         IS_FNUZ=current_platform.fp8_dtype() == torch.float8_e4m3fnuz,
@@ -229,6 +230,7 @@ def cp_gather_indexer_k_quant_cache_triton(
     k_cache_scale = k_cache[:, block_size * head_dim :].view(torch.float32)
     grid = (num_tokens,)
     k_fp8_scale = k_fp8_scale.view(torch.float32)
+    layout = "NORMAL" if block_size == 1 else "SHUFFLE"
     _cp_gather_indexer_quant_cache_kernel[grid](
         k_cache_value,
         k_cache_scale,
@@ -241,7 +243,7 @@ def cp_gather_indexer_k_quant_cache_triton(
         block_table_stride,
         k_cache_value.stride(0),
         k_cache_scale.stride(0),
-        "SHUFFLE",
+        layout,
         head_dim,
         block_tile_size,
         head_tile_size,
@@ -433,7 +435,7 @@ def rocm_fp8_paged_mqa_logits(
                 block_tables,
                 max_model_len,
                 ChunkK=256,
-                Preshuffle=block_size == 64,
+                Preshuffle=block_size > 1,
                 KVBlockSize=block_size,
                 WavePerEU=2,
             )