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PagedAttention and RadixAttention address different parts of LLM serving, so neither is a universal alternative to the other. PagedAttention organizes each request’s key-value (KV) cache into fixed-token blocks; RadixAttention is a runtime optimization for reusing KV cache across work with shared prefixes or other reuse opportunities. The distinction is between managing cache storage and finding chances to reuse cached state.
What problem does KV-cache management solve?
During autoregressive generation, a model builds and consults a KV cache containing attention keys and values from tokens it has already processed. That cache can grow dynamically and consume substantial memory as requests run. The PagedAttention paper identifies inefficient handling as a source of memory waste, including fragmentation and redundant duplication. Kwon and coauthors’ PagedAttention paper frames the challenge as a serving-system problem, not merely a matter of choosing an attention formula.
What PagedAttention does
PagedAttention applies a paging-inspired approach to attention and KV-cache organization. Rather than requiring a request’s cache to occupy one contiguous allocation, the design stores cache tokens in fixed-size blocks and manages those blocks as the cache grows. This helps a serving system allocate memory incrementally and reduce the problems associated with dynamic, variable-length requests.
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The official vLLM design page describes paged-cache blocks as holding a fixed number of tokens. These cache blocks are not the same thing as GPU thread blocks. The page also cautions that its kernel walkthrough is historical and does not describe current vLLM code in full; it is useful for the block concept, not as a complete guide to present-day implementation internals.
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What RadixAttention does
RadixAttention is described in the SGLang paper as a runtime optimization for KV-cache reuse. In a workload where requests share prompt content or other cached context, reuse can avoid doing redundant work for state that is already available. RadixAttention is one optimization in SGLang’s broader runtime, alongside techniques for structured output decoding.
This describes a different primary role from PagedAttention: reuse concerns whether useful KV state can serve more than one execution, while paging concerns how cache state is represented and accessed. The concepts are not inherently mutually exclusive, and their names should not be treated as rival product labels.
PagedAttention vs. RadixAttention at a glance
| Aspect | PagedAttention | RadixAttention |
|---|---|---|
| Primary role | Block-based KV-cache organization and attention access | Runtime optimization for KV-cache reuse |
| Core idea | Store cache tokens in fixed-size blocks that can be managed as requests grow | Reuse cached KV state when execution offers reuse opportunities |
| Source context | Introduced with vLLM as a paging-inspired attention algorithm | Presented as an SGLang runtime optimization for executing structured language-model programs |
| Workload consideration | Dynamic cache growth, memory pressure, and allocation efficiency | Repeated or shared context that creates opportunities to reuse cached state |
| Evidence boundary | The paper reports results for vLLM against the systems it evaluated | The SGLang paper’s headline throughput result is for the overall system, not RadixAttention alone |
Can their published performance numbers be compared?
No. The PagedAttention paper reported 2–4× throughput at the same latency versus the systems it evaluated in 2023. The SGLang paper, revised in 2024, reported up to 6.4× higher throughput than state-of-the-art inference systems on its evaluated tasks and models. Those figures come from separate system evaluations, not a matched experiment isolating PagedAttention against RadixAttention.
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Which approach matters more for a workload?
Consider PagedAttention when cache allocation is the question
If the concern is how to manage growing KV state across variable-length requests and make effective use of serving memory, block-based organization is the relevant idea. The PagedAttention work addresses this storage and access challenge. The paper’s throughput result is evidence about the evaluated vLLM system and comparisons, not a guarantee for every deployment.
Consider RadixAttention when cached context can be reused
If many executions repeat or share prompt prefixes or other context, KV-cache reuse is a workload property worth investigating. RadixAttention is designed as an optimization in that area. The benefit depends on the presence of reuse opportunities; the SGLang paper does not provide a standalone RadixAttention performance figure that predicts results for an arbitrary workload.
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Evaluate the serving system, not just the technique name
For a practical comparison, test the actual systems and versions you may deploy using the same models, hardware, request mix, concurrency, prompt repetition patterns, and latency target. Track throughput and latency alongside memory use, and include both workloads with substantial shared context and workloads with little reuse. That lets you distinguish gains from better cache organization from gains due to cache reuse without assuming either is a universal winner.
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