Head-to-head
SGLang vs vLLM
vLLM leads: the AI models rank it above its rival on 4 of 4 shared leaderboards. Based on how ChatGPT, Claude, Gemini & Grok rank both across 4 shared leaderboards — re-polled on demand, reasoning shown verbatim.
| Leaderboard | SGLang | vLLM |
|---|---|---|
| Best LLM inference server for high-throughput serving | #2 / 7 | #1 / 7 |
| Best LLM inference server for self-hosting | #2 / 7 | #1 / 7 |
| Best open-source LLM inference servers for Kubernetes | #2 / 8 | #1 / 8 |
| Best open-source LLM serving stack | #2 / 7 | #1 / 7 |
Why the models rank SGLang — on best llm inference server for high-throughput serving
“Excellent throughput and latency from RadixAttention, prefix caching, speculative decoding, disaggregated prefill/decode, and strong distributed/MoE serving; narrowly leads for demanding modern workloads with repeated prefixes or structured generation.”
Why the models rank vLLM — on best llm inference server for high-throughput serving
“The de facto standard for high-throughput serving — PagedAttention, continuous batching, prefix caching, speculative decoding, and chunked prefill are mature; broadest model coverage (day-one support for new open-weight releases) and hardware reach (NVIDIA, AMD, TPU, Inferentia, Gaudi); huge production install base means battle-tested OpenAI-compatible serving and the richest ecosystem of deployment tooling (production-stack, Ray Serve, KServe integrations). Ranked first on the assumption the typical practitioner serves varied open-weight models on mixed or NVIDIA hardware and values robustness and community support over the last few percent of throughput.”
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Ranks from the merged 4-model leaderboards · re-polled on demand · methodology