diff --git a/vllm/models/deepseek_v4/amd/model.py b/vllm/models/deepseek_v4/amd/model.py
index 349ff52679..c1a6903937 100644
--- a/vllm/models/deepseek_v4/amd/model.py
+++ b/vllm/models/deepseek_v4/amd/model.py
@@ -957,16 +957,7 @@ class DeepseekV4Model(nn.Module, EagleModelMixin):
         self.hc_dim = self.hc_mult * config.hidden_size
         self.rms_norm_eps = config.rms_norm_eps
 
-        # Three aux streams: one per non-default input GEMM in
-        # DeepseekV4Attention._run_parallel_input_projections
-        # (compressor kv_score, indexer.weights_proj, indexer.compressor
-        # kv_score). fused_wqa_wkv stays on the default stream.
-        # Disable them on ROCm because of hang issues.
-        aux_stream_list = (
-            None
-            if current_platform.is_rocm()
-            else [torch.cuda.Stream() for _ in range(3)]
-        )
+        aux_stream_list = [torch.cuda.Stream() for _ in range(3)]
 
         self.device = current_platform.device_type
         # Reserved topk indices buffer for all Indexer layers to reuse.
diff --git a/vllm/models/deepseek_v4/amd/rocm.py b/vllm/models/deepseek_v4/amd/rocm.py
index b39ad953e7..3cc9827c1f 100644
--- a/vllm/models/deepseek_v4/amd/rocm.py
+++ b/vllm/models/deepseek_v4/amd/rocm.py
@@ -23,6 +23,7 @@ from vllm.models.deepseek_v4.sparse_mla import (
 from vllm.platforms import current_platform
 from vllm.platforms.rocm import _ON_GFX950
 from vllm.triton_utils import tl, triton
+from vllm.utils.multi_stream_utils import execute_in_parallel
 from vllm.v1.attention.backend import (
     CommonAttentionMetadata,
 )
@@ -571,6 +572,199 @@ class DeepseekV4ROCMAiterMLAAttention(DeepseekV4Attention):
         self.register_buffer("_fused_compressor_weight", None, persistent=False)
         self._fused_compressor_split_sizes: tuple[int, int] | None = None
 
+        if self.indexer is None:
+            # Only enable multi-stream overlap for CSA layer now.
+            self.aux_stream_list = None
+        else:
+            # Disable indexer inner overlap.
+            self.indexer.aux_stream = None
+
+    def _enable_csa_multi_stream(self) -> bool:
+        """All CSA multi-stream gates: env var, streams, and capture region.
+
+        Dict metadata marks piecewise cudagraph, whose eager breaks rebuild
+        the attention inputs on the owning stream. Forking side streams
+        there would rely on runtime HIP event sync, which is unreliable in
+        this overlap on ROCm (event waits can hang), so multi-stream only
+        runs where the fork/join becomes static graph edges: inside capture,
+        or with non-dict metadata (full cudagraph or the profile run), which
+        has no eager breaks.
+        """
+        attn_metadata = get_forward_context().attn_metadata
+        return self.aux_stream_list is not None and (
+            torch.cuda.is_current_stream_capturing()
+            or not isinstance(attn_metadata, dict)
+        )
+
+    def _run_sequential_pipeline(
+        self,
+        hidden_states: torch.Tensor,
+        positions: torch.Tensor,
+        o_padded: torch.Tensor,
+    ) -> None:
+        """Disable ROCm streams when the current execution region cannot overlap."""
+        aux_streams = self.aux_stream_list
+        self.aux_stream_list = None
+        try:
+            qr_kv, kv_score, indexer_kv_score, indexer_weights = (
+                self._run_parallel_input_projections(hidden_states)
+            )
+            qr, qr_scale, kv = self._split_qkv_and_norm(qr_kv)
+            self._prepare_and_attn_fn(
+                hidden_states,
+                qr,
+                kv,
+                qr_scale,
+                kv_score,
+                indexer_kv_score,
+                indexer_weights,
+                positions,
+                o_padded,
+            )
+        finally:
+            self.aux_stream_list = aux_streams
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        llama_4_scaling: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # Pre-allocate attention output with FlashMLA-padded head count.
+        # The op writes into `o_padded`; we slice to n_local_heads after.
+        num_tokens = hidden_states.shape[0]
+        o_padded = torch.empty(
+            (num_tokens, self.padded_heads, self.head_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        if self._enable_csa_multi_stream():
+            # The ROCm override consumes these sentinels inside the capture
+            # boundary, moving the stream fan-out ahead of the projections.
+            self._prepare_and_attn_fn(
+                hidden_states,
+                None,
+                None,
+                None,
+                None,
+                None,
+                None,
+                positions,
+                o_padded,
+            )
+        else:
+            self._run_sequential_pipeline(hidden_states, positions, o_padded)
+
+        o = o_padded[:, : self.n_local_heads, :]
+
+        # Inverse-RoPE + wo_a + wo_b output projection (platform-specific).
+        return self._o_proj(o, positions)
+
+    def _prepare_and_attn(
+        self,
+        hidden_states: torch.Tensor,
+        qr: torch.Tensor | None,
+        kv: torch.Tensor | None,
+        qr_scale: torch.Tensor | None,
+        kv_score: torch.Tensor | None,
+        indexer_kv_score: torch.Tensor | None,
+        indexer_weights: torch.Tensor | None,
+        positions: torch.Tensor,
+        o_padded: torch.Tensor,
+    ) -> None:
+        """Run the ROCm CSA fork/join inside the graph capture boundary."""
+        aux_streams = self.aux_stream_list
+        # The sequential pipeline disables aux_stream_list before calling
+        # back with real projection inputs; aux_streams is None ends that
+        # recursion here.
+        if aux_streams is None:
+            saved_streams = self.aux_stream_list
+            self.aux_stream_list = None
+            try:
+                super()._prepare_and_attn(
+                    hidden_states,
+                    cast(torch.Tensor, qr),
+                    cast(torch.Tensor, kv),
+                    qr_scale,
+                    cast(torch.Tensor, kv_score),
+                    cast(torch.Tensor, indexer_kv_score),
+                    cast(torch.Tensor, indexer_weights),
+                    positions,
+                    o_padded,
+                )
+            finally:
+                self.aux_stream_list = saved_streams
+            return
+
+        # Re-check: forward's gate ran inside a captured segment that
+        # _prepare_and_attn_eager (MRV1) then broke, making this region eager.
+        if not self._enable_csa_multi_stream():
+            self._run_sequential_pipeline(hidden_states, positions, o_padded)
+            return
+
+        indexer = self.indexer
+        compressor = self.compressor
+        assert indexer is not None and compressor is not None
+
+        def default_chain():
+            qr_kv = self._fused_wqa_wkv_gemm(hidden_states)
+            qr_out, qr_scale_out, kv_out = self._split_qkv_and_norm(qr_kv)
+            q = self._wq_b_proj(qr_out, qr_scale_out).view(
+                -1, self.n_local_heads, self.head_dim
+            )
+            attn_metadata = get_forward_context().attn_metadata
+            q = self._fused_qnorm_rope_kv_insert(q, kv_out, positions, attn_metadata)
+            return q, qr_out, qr_scale_out, kv_out
+
+        def main_compressor_chain() -> None:
+            score = torch.mm(
+                hidden_states,
+                compressor.fused_wkv_wgate.weight.T,
+                out_dtype=torch.float32,
+            )
+            compressor(score, positions, self.rotary_emb)
+
+        def indexer_compressor_chain() -> None:
+            score = torch.mm(
+                hidden_states,
+                indexer.compressor.fused_wkv_wgate.weight.T,
+                out_dtype=torch.float32,
+            )
+            indexer.compressor(score, positions, self.indexer_rotary_emb)
+
+        (q, qr_out, qr_scale_out, kv_out), _ = execute_in_parallel(
+            default_chain,
+            [main_compressor_chain, indexer_compressor_chain],
+            self.ln_events[0],
+            self.ln_events[1:3],
+            aux_streams[:2],
+            enable=True,
+        )
+
+        indexer_weights_out, _ = indexer.weights_proj(hidden_states)
+        # The indexer compressor already ran on aux stream 1; build queries only.
+        index_q, index_q_scale, weights = indexer(
+            hidden_states,
+            qr_out,
+            None,
+            indexer_weights_out,
+            positions,
+            self.indexer_rotary_emb,
+            qr_scale_out,
+            skip_compressor=True,
+        )
+        self._sparse_indexer_and_attn(
+            hidden_states,
+            index_q,
+            index_q_scale,
+            weights,
+            q,
+            kv_out,
+            positions,
+            o_padded,
+        )
+
     @classmethod
     def get_padded_num_q_heads(cls, num_heads: int) -> int:
         return num_heads
diff --git a/vllm/models/deepseek_v4/attention.py b/vllm/models/deepseek_v4/attention.py
index d32a48cbe4..9c77980965 100644
--- a/vllm/models/deepseek_v4/attention.py
+++ b/vllm/models/deepseek_v4/attention.py
@@ -318,7 +318,6 @@ class DeepseekV4Attention(nn.Module, AttentionLayerBase, ABC):
             # graph and MRV1 produces garbage (#51430).
             self._prepare_and_attn_fn = self._prepare_and_attn_eager
 
-        # Will be None on ROCm for now.
         self.aux_stream_list = aux_stream_list
         # [0]: GEMM start / post-GEMM event0. [1..3]: GEMM done events;
         # [1] doubles as post-GEMM event1. Reuse is safe: GEMM fully joins
@@ -584,7 +583,7 @@ class DeepseekV4Attention(nn.Module, AttentionLayerBase, ABC):
 
         # Keep Q projection and KV insertion on the default stream. The indexer
         # and MLA compressor use aux streams 0 and 1; aux 2 is internal to the
-        # indexer. ROCm runs the same work sequentially without aux streams.
+        # indexer.
         if indexer is not None:
             assert compressor is not None
             q, (indexer_inputs, _) = execute_in_parallel(
@@ -671,7 +670,6 @@ class DeepseekV4Attention(nn.Module, AttentionLayerBase, ABC):
         # fused_wqa_wkv (heaviest) on default; the three lighter input GEMMs
         # on aux streams 0..2 when their owning module exists. ln_events[0]
         # is the fan-out start event; ln_events[1..3] are per-aux done events.
-        # On ROCm, aux_streams is None and execute_in_parallel runs serially.
         aux_fns: list[Callable[[], Any] | None] = [None, None, None]
 
         if self.compressor is not None:
@@ -1055,11 +1053,12 @@ class DeepseekV4Indexer(nn.Module):
         self,
         hidden_states: torch.Tensor,
         qr: torch.Tensor,
-        compressed_kv_score: torch.Tensor,
+        compressed_kv_score: torch.Tensor | None,
         indexer_weights: torch.Tensor,
         positions: torch.Tensor,
         rotary_emb: nn.Module,
         qr_scale: torch.Tensor | None = None,
+        skip_compressor: bool = False,
     ) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]:
         compressor = self.compressor
 
@@ -1072,7 +1071,8 @@ class DeepseekV4Indexer(nn.Module):
             ):
                 # candidates num smaller than topk, every candidate is selected
                 # but we still need to build k cache
-                compressor(compressed_kv_score, positions, rotary_emb)
+                if not skip_compressor:
+                    compressor(compressed_kv_score, positions, rotary_emb)
                 assert self.topk_indices_buffer is not None
                 num_tokens = (
                     indexer_metadata.num_decode_tokens
@@ -1102,15 +1102,18 @@ class DeepseekV4Indexer(nn.Module):
                 use_fp4=self.use_fp4_kv,
             )
 
-        # compressor returns None and writes K to the indexer KV cache; the
-        # join orders that write before indexer_op (skip_k_cache_insert=True).
-        (q_quant, weights), _ = maybe_execute_in_parallel(
-            wq_b_and_q_quant,
-            lambda: compressor(compressed_kv_score, positions, rotary_emb),
-            self.ln_events[0],
-            self.ln_events[1],
-            self.aux_stream,
-        )
+        if not skip_compressor:
+            # compressor returns None and writes K to the indexer KV cache; the
+            # join orders that write before indexer_op (skip_k_cache_insert=True).
+            (q_quant, weights), _ = maybe_execute_in_parallel(
+                wq_b_and_q_quant,
+                lambda: compressor(compressed_kv_score, positions, rotary_emb),
+                self.ln_events[0],
+                self.ln_events[1],
+                self.aux_stream,
+            )
+        else:
+            q_quant, weights = wq_b_and_q_quant()
         if isinstance(q_quant, tuple):
             q, q_scale = q_quant
         else:
diff --git a/vllm/models/deepseek_v4/cpu/cpu_sparse.py b/vllm/models/deepseek_v4/cpu/cpu_sparse.py
index d51e589652..a2c3b00493 100644
--- a/vllm/models/deepseek_v4/cpu/cpu_sparse.py
+++ b/vllm/models/deepseek_v4/cpu/cpu_sparse.py
@@ -198,11 +198,12 @@ class DeepseekV4CPUIndexer(DeepseekV4Indexer):
         self,
         hidden_states: torch.Tensor,
         qr: torch.Tensor,
-        compressed_kv_score: torch.Tensor,
+        compressed_kv_score: torch.Tensor | None,
         indexer_weights: torch.Tensor,
         positions: torch.Tensor,
         rotary_emb: nn.Module,
         qr_scale: torch.Tensor | None = None,
+        skip_compressor: bool = False,
     ) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]:
         """CPU override: no aux streams, so wq_b_and_q_quant and the
         compressor run straight-line instead of through