When does paged attention actually pay off?
Paged attention shines when your serving workload has long sequences or many concurrent requests, because it eliminates the memory waste that comes from monolithic KV cache allocations. In a 2024 study, paged attention achieved 2–4× higher throughput than state-of-the-art systems like FasterTransformer and Orca, with the gains growing for longer sequences and larger models [4]. That means if you're serving chatbots with long histories or document-grounded Q&A, paged attention is a clear win.
A 2025 benchmark on an NVIDIA L4 GPU showed that with paged attention, inference latency grew only linearly (~2×) when sequence length increased from 128 to 2048 tokens, compared to exponential growth without caching [2]. So the benefit is most pronounced when contexts are long and variable—exactly the conditions where retrieval-augmented generation (RAG) or multi-turn tool use would add even more tokens to the context.
Why pair paged attention with symbolic tools, simulators, or retrieval?
Paged attention is a memory-management technique, not a reasoning engine. It frees up GPU memory and speeds up attention computation, but it doesn't help the model decide what to retrieve or how to run a simulation. So you combine them when your task needs both: long-context handling (where paged attention helps) and external knowledge or multi-step logic (where tools help). For example, a retrieval-augmented system that pulls in many documents will create a large KV cache; paged attention keeps that cache efficient, while the retrieval component ensures the model gets the right information.
A 2025 systems paper built a modular serving stack on top of vLLM's paged attention, adding components like compression routers and output routers to integrate prompt compression and other optimizations [5]. This shows that paged attention is designed to be a foundation you can layer other tools on, not a replacement for them. Similarly, a 2026 study on TPUs integrated paged attention into vLLM and SGLang as the primary backend, demonstrating that it works as a production-grade base for serving systems that may include additional features [1].
When should you skip the combination?
If your workload is short-context and single-turn, paged attention's benefits shrink, and adding retrieval or simulators may just add latency without payoff. A 2026 study found that for short sequences (512–4096 tokens), paged attention gave 2.0–2.5× throughput improvement over a baseline, but at the cost of 2.2× higher peak memory reservation [3]. That memory overhead might not be worth it if your contexts are short and you don't need external tools.
Also, if your bottleneck is not memory but the attention computation itself, you might need algorithmic sparsity rather than paging. A 2025 paper on progressive sparse attention showed that by adaptively reducing KV cache usage per token and layer, it cut KV cache usage by up to 8.8× and increased throughput by up to 2.0× compared to systems without sparse attention [6]. That's a different lever than paged attention—so if your problem is attention speed, not memory fragmentation, consider sparse attention instead of or in addition to paging.
About These Sources
This answer is built on 6 studies (1 peer-reviewed, 5 preprints) — published from 2024 to 2026, 6 from 2024 or later — selected as the most relevant from 8 studies that passed quality screening, drawn from 51 papers retrieved from a database of over 500 million.
Sources used in this answer
Ragged Paged Attention: A High-Performance and Flexible LLM Inference Kernel for TPU
Ragged Paged Attention (RPA) on TPUs achieves up to 86% memory bandwidth utilization in decode and 73% model FLOPs utilization in prefill, and is integrated as the primary TPU backend in vLLM and SGLang, showing paged attention works on non-GPU hardware.
Paged Attention Meets FlexAttention: Unlocking Long-Context Efficiency in Deployed Inference
Integrating PagedAttention with FlexAttention in IBM's FMS reduces inference latency growth to ~2× for sequence lengths 128–2048 tokens, compared to exponential growth without caching, on an NVIDIA L4 GPU.
Scaling Long-Context LLMs via Unified KV Cache Optimization: A Comparative Study of Paged Attention and Quantization
In a single-batch comparison on Mistral-7B, vLLM's PagedAttention delivers 2.0–2.5× throughput improvement over an FP16 baseline but uses 2.2× higher peak memory, while KV quantization reduces memory by <2% and degrades throughput by 55–89%.
Open-AI model Efficient Memory Reduce Management for the Large Language Models (LLMs) Serving with Paged Attention of sharing the KV Cashes
PagedAttention achieves near-zero KV cache waste and flexible sharing, improving throughput by 2–4× over FasterTransformer and Orca, with larger gains for longer sequences and larger models.
ML and systems co-design for resource-efficient LLM inference serving
A systems co-design project builds modular components (compression routers, output routers) on top of vLLM's paged attention to integrate optimizations like prompt compression and quantization, aiming for adaptive serving.
Progressive Sparse Attention: Algorithm and System Co-design for Efficient Attention in LLM Serving
Progressive Sparse Attention (PSA) adaptively adjusts KV cache budgets per token and layer, reducing KV cache usage by up to 8.8× and increasing throughput by up to 2.0× compared to systems without sparse attention.