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What is the difference between PagedAttention and continuous batching?
Autoregressive language models reuse keys and values from earlier tokens while generating the next token. That KV cache grows during a request and can consume substantial accelerator memory. PagedAttention changes how that cache is allocated and mapped; continuous batching changes how the scheduler selects active requests over time.
| Dimension | PagedAttention | Continuous batching |
|---|---|---|
| Main concern | KV-cache memory allocation and sharing | Keeping execution capacity occupied as requests finish and arrive |
| Mechanism | Stores KV state in fixed-token blocks, allocates physical blocks as needed, and maps logical blocks to physical blocks | Updates the active request set at generation iterations, allowing completed sequences to leave and waiting work to enter |
| Potential benefit | Less wasted cache capacity and the option to share common cache state | Less idle time waiting for the longest request in a fixed batch; potentially better utilization with varied request lengths |
| Important trade-off | Block-table indirection and kernel implementation can add overhead; block size involves trade-offs | Results depend on request mix, scheduler policy, implementation, capacity, and serving constraints |
How does PagedAttention work?
A conventional approach may reserve a large contiguous region for a request’s maximum possible sequence length. That can leave memory unused and create internal or external fragmentation. PagedAttention divides KV state into fixed-size blocks and allocates physical blocks as tokens arrive. Blocks belonging to one logical sequence do not have to sit next to each other in physical memory.
The vLLM documentation summarizes the core idea as partitioning each request’s KV cache into KV blocks. See vLLM’s Automatic Prefix Caching documentation and the project’s PagedAttention explainer.
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Block management can also support cache sharing. For example, multiple outputs that use the same prompt may share prefix state instead of duplicating it. vLLM’s automatic prefix caching documentation describes reusing blocks for matching prefixes; blocks with no active references may be evicted when the cache is full. Prefix caching is a cache-reuse feature built on block management, not a batching policy.
How does continuous batching work?
In a fixed batch, requests can have different prompt and output lengths. When a sequence finishes before the others, its slot may sit idle until the batch’s remaining work completes. Continuous batching instead makes scheduling decisions at generation iterations: completed sequences can leave the active set, while waiting requests can enter when the implementation has capacity.
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Anyscale also calls this approach dynamic batching or batching with iteration-level scheduling. Its purpose is to keep useful work moving through the model; it does not specify how the KV cache is laid out. The precise admission and scheduling behavior depends on the serving system.
How do PagedAttention and continuous batching work together?
They operate at separate layers and can be composed. Continuous batching determines which sequences are scheduled at each iteration. PagedAttention determines how those sequences’ KV state occupies and shares memory. vLLM’s current documentation lists both among its serving features, alongside capabilities such as chunked prefill, prefix caching, speculative decoding, streaming, and distributed inference. That feature list describes the project’s implementation, not an independent performance evaluation. See the current vLLM documentation.
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This distinction matters operationally: more efficient cache allocation may let a system accommodate more active state, while iteration-level scheduling may reduce idle execution capacity as requests finish. Neither mechanism alone guarantees a particular throughput or latency improvement, and cache reuse does not itself admit or schedule new requests.
What performance results have been published?
Published results illustrate possible effects in particular experiments; they are not forecasts for a new deployment. The figures below use different baselines and conditions and should not be combined into a single ranking.
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- PagedAttention system results: Kwon and coauthors’ 2023 SOSP paper reports 2–4× throughput at the same latency against FasterTransformer and Orca across its evaluated models and workloads. It reports larger gains for longer sequences, larger models, and more complex decoding algorithms. The same paper measured 20–26% higher attention-kernel latency for its PagedAttention kernels than a highly optimized FasterTransformer implementation in a microbenchmark; that kernel-level result did not prevent better end-to-end performance in the paper’s evaluated scenarios. Read the paper.
- Continuous-batching results: Anyscale reported up to 23× throughput for continuous batching combined with continuous-batching-specific memory optimizations using vLLM in its 2023 benchmark. It separately reported 8× over naive batching for selected tested systems. These are results from that benchmark, not universal guarantees. Read Anyscale’s benchmark discussion.
- Memory-waste claim: The vLLM project’s 2023 explainer reported under 4% memory waste for its described block-allocation scheme. This is the project’s reported figure, not a property established for every paged-cache implementation or workload. See the explainer.
How should you compare them for a deployment?
Benchmark the serving system on a matched workload rather than assuming that a published multiplier will transfer. Keep the model, hardware, prompt and output lengths, arrival rate, concurrency, and latency target consistent. Compare end-to-end throughput and latency as well as relevant memory use; a kernel microbenchmark alone does not establish system-level performance.
When diagnosing a bottleneck, ask which layer is implicated. If requests cannot fit because of KV-cache capacity or memory fragmentation, investigate cache allocation and reuse. If execution capacity goes idle while shorter requests finish and other work waits, investigate iteration-level scheduling. A deployment may need both kinds of improvement.
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