SGLang
A high-performance serving engine notable for fast structured output and efficient KV-cache sharing. Its RadixAttention makes it particularly strong for prefix-heavy RAG and multi-turn chat, where reused context is the main lever.
Strengths
- Efficient KV-cache reuse, strong for RAG and multi-turn chat
- Fast constrained and structured output generation
- OpenAI-compatible API for easy integration
Weaknesses
- Steeper learning curve than a desktop app
- NVIDIA-focused, and needs a GPU to be worthwhile
- A newer project than vLLM, with a smaller community
At a glance
- Licence
- Apache 2.0
- Pricing
- Free
- Platforms
- Linux, Docker
- GPU required
- Yes
Links
Works with
Related guides
See also
Glossary
Our coverage
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- Mistral Large 4 'Le Chonk' open weights have landed, under a new modified-MIT licence
- Tencent's Hunyuan 4 flagship ships as an Apache 2.0 open model, with no territory clause
- DeepSeek releases V4.1 Flash, an MIT-licensed multimodal model with an unusual architecture
- GLM-5.3 open weights land, under a bespoke licence rather than MIT
- Liquid AI releases LFM2.5-VL-3B, an open-weight vision model built to run on-device
Entry last verified 30 July 2026.