diff --git a/vllm/v1/attention/backends/triton_attn.py b/vllm/v1/attention/backends/triton_attn.py
index c4922a716bc..c48193c5d23 100644
--- a/vllm/v1/attention/backends/triton_attn.py
+++ b/vllm/v1/attention/backends/triton_attn.py
@@ -7,6 +7,9 @@
 from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata, AttentionType)
+from vllm.attention.ops.chunked_prefill_paged_decode import (
+    chunked_prefill_paged_decode)
+from vllm.attention.ops.paged_attn import PagedAttention
 from vllm.attention.ops.triton_unified_attention import unified_attention
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
@@ -149,19 +152,40 @@ def forward(
         # Whenever making a change in this method, please benchmark the
         # performance to make sure it does not introduce any overhead.
 
+        num_queries_per_kv = query.shape[1] // key.shape[1]
+        use_prefill_decode_attn = (num_queries_per_kv &
+                                   (num_queries_per_kv - 1)) != 0
+
         num_actual_tokens = attn_metadata.num_actual_tokens
 
-        key_cache, value_cache = kv_cache.unbind(0)
-        torch.ops._C_cache_ops.reshape_and_cache_flash(
-            key,
-            value,
-            key_cache,
-            value_cache,
-            attn_metadata.slot_mapping,
-            self.kv_cache_dtype,
-            layer._k_scale,
-            layer._v_scale,
-        )
+        if use_prefill_decode_attn:
+            key_cache, value_cache = PagedAttention.split_kv_cache(
+                kv_cache, self.num_kv_heads, self.head_size)
+
+            # Reshape the input keys and values and store them in the cache.
+            PagedAttention.write_to_paged_cache(
+                key,
+                value,
+                key_cache,
+                value_cache,
+                attn_metadata.slot_mapping,
+                self.kv_cache_dtype,
+                layer._k_scale,
+                layer._v_scale,
+            )
+
+        else:
+            key_cache, value_cache = kv_cache.unbind(0)
+            torch.ops._C_cache_ops.reshape_and_cache_flash(
+                key,
+                value,
+                key_cache,
+                value_cache,
+                attn_metadata.slot_mapping,
+                self.kv_cache_dtype,
+                layer._k_scale,
+                layer._v_scale,
+            )
 
         if self.kv_cache_dtype.startswith("fp8"):
             key_cache = key_cache.view(self.fp8_dtype)
@@ -196,26 +220,47 @@ def forward(
             max_seqlen_k = attn_metadata.max_seq_len
             block_table = attn_metadata.block_table
 
-        descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])
-
-        unified_attention(
-            q=query[:num_actual_tokens],
-            k=key_cache,
-            v=value_cache,
-            out=output[:num_actual_tokens],
-            cu_seqlens_q=cu_seqlens_q,
-            max_seqlen_q=max_seqlen_q,
-            seqused_k=seqused_k,
-            max_seqlen_k=max_seqlen_k,
-            softmax_scale=self.scale,
-            causal=True,
-            alibi_slopes=self.alibi_slopes,
-            window_size=self.sliding_window,
-            block_table=block_table,
-            softcap=self.logits_soft_cap,
-            q_descale=None,  # Not supported
-            k_descale=layer._k_scale.expand(descale_shape),
-            v_descale=layer._v_scale.expand(descale_shape),
-        )
+        if use_prefill_decode_attn:
+            # Compute attention and update output up to `num_actual_tokens`.
+            chunked_prefill_paged_decode(query=query[:num_actual_tokens],
+                                         key=key[:num_actual_tokens],
+                                         value=value[:num_actual_tokens],
+                                         output=output[:num_actual_tokens],
+                                         kv_cache_dtype=self.kv_cache_dtype,
+                                         key_cache=key_cache,
+                                         value_cache=value_cache,
+                                         block_table=block_table,
+                                         query_start_loc=cu_seqlens_q,
+                                         seq_lens=seqused_k,
+                                         max_seq_len=max_seqlen_k,
+                                         max_query_len=max_seqlen_q,
+                                         k_scale=layer._k_scale,
+                                         v_scale=layer._v_scale,
+                                         alibi_slopes=self.alibi_slopes,
+                                         sliding_window=self.sliding_window[0],
+                                         sm_scale=self.scale)
+
+        else:
+            descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])
+
+            unified_attention(
+                q=query[:num_actual_tokens],
+                k=key_cache,
+                v=value_cache,
+                out=output[:num_actual_tokens],
+                cu_seqlens_q=cu_seqlens_q,
+                max_seqlen_q=max_seqlen_q,
+                seqused_k=seqused_k,
+                max_seqlen_k=max_seqlen_k,
+                softmax_scale=self.scale,
+                causal=True,
+                alibi_slopes=self.alibi_slopes,
+                window_size=self.sliding_window,
+                block_table=block_table,
+                softcap=self.logits_soft_cap,
+                q_descale=None,  # Not supported
+                k_descale=layer._k_scale.expand(descale_shape),
+                v_descale=layer._v_scale.expand(descale_shape),
+            )
 
         return output