diff --git a/vllm/model_executor/models/config.py b/vllm/model_executor/models/config.py
index a9e6815a49..cb01a12efd 100644
--- a/vllm/model_executor/models/config.py
+++ b/vllm/model_executor/models/config.py
@@ -71,8 +71,11 @@ class UnlimitedOCRForCausalLMConfig(VerifyAndUpdateConfig):
           2. ``--attention-config '{"backend": "FLEX_ATTENTION"}'``
              → FlexAttention R-SWA via Triton block mask.
 
-          3. ``--attention-config '{"backend": "auto"}'`` (or omitted)
-             → Auto-detect: FA4 if available (H20/H100 SM90), else FlexAttention.
+          3. ``--attention-config '{"backend": "TRITON_ATTN"}'``
+             → Triton unified attention with an R-SWA decode mask.
+
+          4. ``--attention-config '{"backend": "auto"}'`` (or omitted)
+             → Auto-detect: FA4 if available (H20/H100 SM90), else TritonAttention.
 
         Regardless of backend, prefix caching is disabled for this model: R-SWA
         decode-phase KV is not a pure causal function of the prefix (so decode
@@ -96,7 +99,7 @@ class UnlimitedOCRForCausalLMConfig(VerifyAndUpdateConfig):
             attn_config.backend = (
                 AttentionBackendEnum.FLASH_ATTN
                 if fa4_available
-                else AttentionBackendEnum.FLEX_ATTENTION
+                else AttentionBackendEnum.TRITON_ATTN
             )
             logger.info(
                 "Unlimited-OCR: auto-selected attention backend=%s (fa4_available=%s).",
@@ -110,8 +113,8 @@ class UnlimitedOCRForCausalLMConfig(VerifyAndUpdateConfig):
                 raise RuntimeError(
                     "Unlimited-OCR: --attention-config backend=FLASH_ATTN "
                     "requires FA4 (rswa_mask_mod), but FA4 is not available on "
-                    "this device/installation.  Use backend=FLEX_ATTENTION or "
-                    "upgrade vllm-flash-attn."
+                    "this device/installation. Use backend=TRITON_ATTN or "
+                    "FLEX_ATTENTION, or upgrade vllm-flash-attn."
                 )
             # On SM90 (H20), the default FA version is FA3 regardless of FA4
             # availability (FA4 is only auto-upgraded when head_size > 256).
@@ -130,6 +133,11 @@ class UnlimitedOCRForCausalLMConfig(VerifyAndUpdateConfig):
                 attn_config.flash_attn_version,
             )
 
+        elif attn_config.backend == AttentionBackendEnum.TRITON_ATTN:
+            logger.info(
+                "Unlimited-OCR: TritonAttention — R-SWA via unified attention mask."
+            )
+
         elif attn_config.backend == AttentionBackendEnum.FLEX_ATTENTION:
             logger.info(
                 "Unlimited-OCR: FlexAttention — R-SWA via Triton block mask%s.",
@@ -144,7 +152,7 @@ class UnlimitedOCRForCausalLMConfig(VerifyAndUpdateConfig):
             raise ValueError(
                 f"Unlimited-OCR: unsupported attention backend "
                 f"{attn_config.backend!r} for R-SWA. "
-                "Use FLASH_ATTN (FA4) or FLEX_ATTENTION."
+                "Use FLASH_ATTN (FA4), TRITON_ATTN or FLEX_ATTENTION."
             )
 
         # R-SWA windows the *generated* tokens, so a decode-token's KV is not a
diff --git a/vllm/model_executor/models/unlimited_ocr.py b/vllm/model_executor/models/unlimited_ocr.py
index 06dc02512f..0f29337314 100644
--- a/vllm/model_executor/models/unlimited_ocr.py
+++ b/vllm/model_executor/models/unlimited_ocr.py
@@ -21,15 +21,11 @@ backbone architecture, FlexAttention for R-SWA, vision encoder backend, and
 Attention backend: the reference applies Reference Sliding Window Attention
 (R-SWA) -- the prompt/image tokens form a globally-visible prefix while the
 *generated* tokens additionally attend only a fixed sliding window (128) of
-recent tokens. We reproduce this (Level 1: full KV cache + custom mask) by
-forcing the language model onto the FlexAttention backend and installing an
-R-SWA ``mask_mod``. FlexAttention is the only backend able to express the
-"global prefix + sliding window" mask; FlashAttention-3 / Triton only support a
-uniform window (and additionally crash or compute incorrectly on this decoder's
-10-head,
-head_dim-128 shape), and FlashInfer's paged decode exposes no custom mask. The
-window size is published via ``model_config.rswa_window``, which the model
-runner reads to plumb per-request prefix lengths into the FlexAttention mask.
+recent tokens. We reproduce this with backend-specific custom masks: FA4 uses a
+``mask_mod``, FlexAttention uses a Triton block mask, and TritonAttention uses a
+unified-attention decode mask. FlashInfer's paged decode exposes no custom mask.
+The window size is published via ``model_config.rswa_window``, which the model
+runner reads to plumb per-request prefix lengths into the attention metadata.
 
 The *vision encoder* (DeepEncoder's CLIP stage, head_dim 64) is unaffected and
 does not use R-SWA: it runs a single full-attention prefill pass. FlashAttention,
diff --git a/vllm/v1/attention/backends/triton_attn.py b/vllm/v1/attention/backends/triton_attn.py
index b0030d27d9..6ba0c94537 100644
--- a/vllm/v1/attention/backends/triton_attn.py
+++ b/vllm/v1/attention/backends/triton_attn.py
@@ -94,6 +94,8 @@ class TritonAttentionMetadata:
     prefix_scheduler_metadata: torch.Tensor | None = None
     mm_prefix_range: dict[int, list[tuple[int, int]]] | None = None
     mm_prefix_range_tensor: torch.Tensor | None = None
+    rswa_prefix_lens: torch.Tensor | None = None
+    rswa_window: int | None = None
 
 
 class TritonAttentionMetadataBuilder(AttentionMetadataBuilder[TritonAttentionMetadata]):
@@ -171,6 +173,14 @@ class TritonAttentionMetadataBuilder(AttentionMetadataBuilder[TritonAttentionMet
             dtype=torch.float32,
             device=device,
         )
+        self.rswa_window = model_config.rswa_window
+        self.persistent_rswa_prefix_lens: torch.Tensor | None = None
+        if self.rswa_window is not None:
+            self.persistent_rswa_prefix_lens = torch.empty(
+                vllm_config.scheduler_config.max_num_seqs,
+                dtype=torch.int32,
+                device=device,
+            )
 
     def build_for_cudagraph_capture(
         self, common_attn_metadata: CommonAttentionMetadata
@@ -244,6 +254,17 @@ class TritonAttentionMetadataBuilder(AttentionMetadataBuilder[TritonAttentionMet
                 mm_ranges, num_reqs, seq_lens.device
             )
 
+        rswa_prefix_lens = common_attn_metadata.rswa_prefix_lens
+        if self.rswa_window is not None and rswa_prefix_lens is not None:
+            assert self.persistent_rswa_prefix_lens is not None
+            rswa_prefix_lens = rswa_prefix_lens.to(
+                device=self.device, dtype=torch.int32, non_blocking=True
+            )
+            persistent_prefix_lens = self.persistent_rswa_prefix_lens[:num_reqs]
+            persistent_prefix_lens.copy_(rswa_prefix_lens[:num_reqs])
+            attn_metadata.rswa_prefix_lens = persistent_prefix_lens
+            attn_metadata.rswa_window = self.rswa_window
+
         return attn_metadata
 
 
@@ -671,6 +692,8 @@ class TritonAttentionImpl(AttentionImpl):
             sinks=self.sinks,
             output_scale=output_scale,
             mm_prefix_range=mm_prefix_range_tensor,
+            rswa_prefix_lens=attn_metadata.rswa_prefix_lens,
+            rswa_window=attn_metadata.rswa_window,
             kv_quant_mode=self._kv_quant_mode,
             k_scale_cache=k_scale_cache,
             v_scale_cache=v_scale_cache,
diff --git a/vllm/v1/attention/ops/triton_attention_helpers.py b/vllm/v1/attention/ops/triton_attention_helpers.py
index e753d53cdf..b2667935c6 100644
--- a/vllm/v1/attention/ops/triton_attention_helpers.py
+++ b/vllm/v1/attention/ops/triton_attention_helpers.py
@@ -281,6 +281,9 @@ def compute_kv_seq_mask(
     USE_CAUSAL: tl.constexpr = True,
     USE_PER_SEQ_CAUSAL: tl.constexpr = False,
     per_seq_causal_ptr=None,
+    rswa_prefix_lens_ptr=None,
+    R_SWA_WINDOW: tl.constexpr = 0,
+    USE_R_SWA: tl.constexpr = False,
     CHUNK_LOOKBACK: tl.constexpr = -1,
     CHUNK_SIZE: tl.constexpr = -1,
     MM_PREFIX_CLAMP_SW: tl.constexpr = False,
@@ -322,7 +325,7 @@ def compute_kv_seq_mask(
             (query_abs_pos // CHUNK_SIZE - seq_offset[None, :] // CHUNK_SIZE)
             <= CHUNK_LOOKBACK
         )
-    elif SLIDING_WINDOW > 0:
+    elif SLIDING_WINDOW > 0 and not USE_R_SWA:
         sw_left = (query_abs_pos - seq_offset) < SLIDING_WINDOW
         if USE_PER_SEQ_CAUSAL:
             sw_right = (seq_offset[None, :] - query_abs_pos) < SLIDING_WINDOW
@@ -333,6 +336,12 @@ def compute_kv_seq_mask(
         else:
             seq_mask = seq_mask & sw_left
 
+    if USE_R_SWA:
+        prefix_len = tl.load(rswa_prefix_lens_ptr + seq_idx)
+        in_prefix = seq_offset[None, :] < prefix_len
+        in_window = (query_abs_pos - seq_offset) < R_SWA_WINDOW
+        seq_mask = seq_mask & (in_prefix | in_window)
+
     # PrefixLM: extend mask with bidirectional ranges for multimodal tokens.
     # Default (MM_PREFIX_CLAMP_SW=False): applied AFTER sliding window so
     # mm_prefix ranges override the SW restriction -> (causal AND SW) OR mm.
diff --git a/vllm/v1/attention/ops/triton_unified_attention.py b/vllm/v1/attention/ops/triton_unified_attention.py
index a1c9abde88..a13ae96a7a 100644
--- a/vllm/v1/attention/ops/triton_unified_attention.py
+++ b/vllm/v1/attention/ops/triton_unified_attention.py
@@ -221,6 +221,9 @@ def kernel_unified_attention(
     USE_MM_PREFIX: tl.constexpr,  # bool
     MAX_MM_RANGES: tl.constexpr,  # int
     mm_prefix_range_ptr,
+    rswa_prefix_lens_ptr,
+    R_SWA_WINDOW: tl.constexpr,  # int
+    USE_R_SWA: tl.constexpr,  # bool
     stride_k_cache_0: tl.int64,  # int
     stride_k_cache_1: tl.int64,  # int
     stride_k_cache_2: tl.int64,  # int
@@ -395,7 +398,7 @@ def kernel_unified_attention(
         BLOCK_Q,
         num_queries_per_kv,
         SLIDING_WINDOW,
-        USE_MM_PREFIX,
+        USE_MM_PREFIX or USE_R_SWA,
         IS_3D,
         USE_CAUSAL,
         USE_PER_SEQ_CAUSAL,
@@ -511,6 +514,9 @@ def kernel_unified_attention(
             USE_CAUSAL,
             USE_PER_SEQ_CAUSAL,
             per_seq_causal_ptr,
+            rswa_prefix_lens_ptr,
+            R_SWA_WINDOW,
+            USE_R_SWA,
             CHUNK_LOOKBACK,
             CHUNK_SIZE,
             MM_PREFIX_CLAMP_SW,
@@ -812,6 +818,10 @@ def unified_attention(
     sinks=None,
     # Optional tensor for prefix lengths (PrefixLM support)
     mm_prefix_range=None,
+    # R-SWA support: prefix tokens stay globally visible, generated tokens use
+    # a fixed sliding window.
+    rswa_prefix_lens=None,
+    rswa_window: int | None = None,
     use_alibi_sqrt=False,
     # KV cache quantization mode and per-token-head scale caches.
     kv_quant_mode: KVQuantMode = KVQuantMode.NONE,
@@ -897,6 +907,8 @@ def unified_attention(
                 f"Unsupported mm_prefix_range shape: {mm_prefix_range.shape}"
             )
 
+    use_rswa = rswa_window is not None and rswa_prefix_lens is not None
+
     use_alibi_slopes = alibi_slopes is not None
     use_qq_bias = qq_bias is not None
 
@@ -1111,6 +1123,9 @@ def unified_attention(
         USE_MM_PREFIX=use_mm_prefix,
         MAX_MM_RANGES=max_mm_ranges,
         mm_prefix_range_ptr=mm_prefix_range,
+        rswa_prefix_lens_ptr=rswa_prefix_lens if use_rswa else seqused_k,
+        R_SWA_WINDOW=rswa_window or 0,
+        USE_R_SWA=use_rswa,
         stride_k_cache_0=k.stride(0),
         stride_k_cache_1=k.stride(1),
         stride_k_cache_2=k.stride(2),