SGLang vs. vLLM: Inference runtimes and RadixAttention
benchmarks
| Source: Mastodon | Original article
A deep analysis compares SGLang and vLLM inference runtimes, spotlighting RadixAttention, speculative decoding and Python throughput benchmarks.
A new comparative study released this week pits SGLang against vLLM, the two leading inference runtimes that power large‑language‑model (LLM) services in 2026. The analysis dives into the core architectural difference that drives performance: SGLang’s RadixAttention tree‑based prefix caching versus vLLM’s PagedAttention KV paging. Both frameworks were benchmarked on Nvidia H100 and H200 hardware, measuring throughput, latency, structured JSON generation and the cost of processing a million tokens.
SGLang’s design couples a front‑end language for chaining prompts and controlling flow with a runtime engine built around RadixAttention. The study shows that RadixAttention’s prefix‑reuse strategy can keep more of the attention cache on‑chip, reducing memory traffic and delivering higher token‑per‑second rates in multi‑GPU clusters. vLLM, by contrast, relies on paging the KV cache in and out of GPU memory, a technique that scales well for very long contexts but can incur higher latency when the cache thrashes.
The benchmarks reveal that on an 8‑GPU H100 cluster, SGLang outpaces vLLM on workloads that generate structured output such as JSON schemas, while vLLM retains an edge on extremely long‑context generation where paging mitigates memory pressure. Cost analysis indicates that the higher throughput of RadixAttention translates into lower per‑token expenses for typical inference patterns, a factor that could sway enterprises weighing sovereign‑AI deployments against hosted alternatives.
The findings matter for developers and firms that must choose an inference stack for production agents, chat services or data‑intensive pipelines. As LLM workloads become more heterogeneous, the trade‑off between raw speed and flexible context handling will shape infrastructure spend.
Looking ahead, the community will watch for updates to RadixAttention that aim to extend its advantage to longer contexts, and for vLLM’s roadmap to address the latency gap in structured decoding. Adoption trends in cloud‑native AI platforms and the emergence of new GPU generations will further test which runtime becomes the default for high‑scale inference.
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