SGLang: what it is, what it is for, and when to use something else
Inference server optimised for structured generation and prefix reuse.
At a glance
| Capability | SGLang |
|---|---|
| Licence | Apache 2.0 |
| Implementation | Python + CUDA |
| Runs on | GPU only |
| Continuous batching | Yes |
| Paged attention | Yes |
| Tensor parallelism | Yes |
| Quantisation | AWQ, GPTQ, FP8 |
| OpenAI-compatible API | Yes |
SGLang is a serving engine aimed at the workloads that dominate agent systems: many requests sharing a long system prompt, multi-turn conversations, and outputs that must conform to a schema. Its central mechanism is RadixAttention, which keeps the KV cache in a radix tree so that any shared prefix across requests is computed once and reused rather than recomputed per request.
For agent traffic this matters more than raw kernel speed. When a thousand requests share a two-thousand-token system prompt, prefix reuse removes most of the prefill work, and prefill is where a large share of the compute goes. It also implements fast constrained decoding, so JSON-schema-conforming output costs far less than generate-then-validate-then-retry.
It overlaps substantially with vLLM and both are converging in features. The practical selection rule: choose SGLang when your traffic has heavy prefix sharing or strict structured-output requirements, and vLLM when you want the larger ecosystem and longer production track record.
Where it stops being the right tool
A newer project than vLLM with a correspondingly smaller operational track record and ecosystem.
Frequently asked
- What is SGLang used for?
- Agent workloads, long shared system prompts, constrained/JSON output, and multi-turn traffic where prefixes repeat heavily.
- What are the limitations of SGLang?
- A newer project than vLLM with a correspondingly smaller operational track record and ecosystem.
- Does SGLang need a GPU?
- Yes. SGLang targets GPU execution and is not a practical CPU inference path.
Compare with
- Ollama — The simplest way to run open models on your own machine.
- LM Studio — A desktop GUI for discovering, running and chatting with local models.
- vLLM — High-throughput inference server built around PagedAttention.
- llama.cpp — Portable C/C++ inference that runs open models almost anywhere.
Project home: https://docs.sglang.ai