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SGLang

Fast LLM serving framework — efficient serving for LLMs and vision-language models

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SGLang (Structured Generation Language) is an open-source framework for fast LLM serving and inference. It achieves 5–10× throughput improvements over naive inference through RadixAttention (efficient KV cache reuse), continuous batching, and structured generation with constrained decoding. Supports Llama, Mistral, Gemma, Qwen, and most open-source LLMs.

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38,782 GitHub stars source
as of 2026-11-12

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What we know

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Why it matters

  • RadixAttention's prefix caching is a genuine algorithmic innovation — 5–10× throughput improvement for prefix-heavy workloads.
  • OpenAI-compatible API means drop-in replacement for vLLM or any OpenAI-compatible server.
  • Developed by LMSYS (Vicuna, Chatbot Arena creators) with strong academic ML infrastructure credentials.
  • Particularly valuable for RAG systems where many queries share the same document context prefix.

Key capabilities

  • RadixAttention: Automatic KV cache reuse for requests sharing common prefixes — faster inference for many-shot and RAG workloads.
  • Continuous batching: Process multiple requests simultaneously for high GPU utilization.
  • OpenAI-compatible API: Drop-in replacement for OpenAI API endpoints.
  • Structured generation: Generate JSON, regex-constrained outputs, and structured formats efficiently.
  • Parallel generation: SGLang DSL for writing LLM programs with branching and parallel calls.
  • Multi-modal: Supports vision-language models (LLaVA, InternVL, etc.).
  • Model support: Llama, Mistral, Gemma, Qwen, Phi, Yi, and most Hugging Face transformer models.
  • Quantization: Support for GPTQ, AWQ, and FP8 quantization for memory efficiency.

Technical notes

  • License: Apache 2.0 (open source)
  • GitHub: github.com/sgl-project/sglang (7K+ stars)
  • Install: pip install sglang[all]
  • GPU: NVIDIA CUDA; AMD ROCm (experimental)
  • API: OpenAI-compatible REST API
  • Models: Llama, Mistral, Gemma, Qwen, Phi, LLaVA, and more
  • Developed by: LMSYS team (UC Berkeley, CMU, UCSD)

Ideal for

  • ML engineers serving open-source LLMs who need high throughput and have workloads with shared prefixes.
  • RAG applications where efficient prefix caching significantly improves serving efficiency.
  • Researchers and teams who want a more feature-rich alternative to vLLM.

Not ideal for

  • Users who need a simple desktop chat UI — LM Studio or Open WebUI are more appropriate.
  • Small-scale inference where deployment complexity isn't worth the optimization benefits.
  • Windows or non-NVIDIA GPU environments where full support is limited.

See also

  • vLLM — Primary competitor; pioneers PagedAttention; more established community.
  • Text Generation WebUI — Easier setup for local inference with UI; less optimized for throughput.
  • LM Studio — Desktop app for running local LLMs; no production serving optimization.

FAQ

What is SGLang?

SGLang (Structured Generation Language) is a Python library for efficient LLM inference serving. It's designed for production serving of open-source LLMs with high throughput. Key innovations: RadixAttention (reuses cached KV states for requests with shared prefixes — dramatically faster for many-shot prompts), continuous batching, and a high-level DSL for writing LLM programs with parallel generation.

How does SGLang compare to vLLM?

Both SGLang and vLLM are high-throughput LLM inference servers. vLLM pioneered PagedAttention for efficient memory management. SGLang adds RadixAttention (efficient prefix caching for requests sharing common prefixes), and a structured generation DSL for LLM programs. For workloads with many-shot examples, system prompts, or RAG (where many requests share the same prefix), SGLang can outperform vLLM. Both are production-grade choices.

What is RadixAttention?

RadixAttention is SGLang's key innovation for efficient KV cache management. When multiple requests share a common prefix (e.g., the same system prompt or few-shot examples), RadixAttention reuses the cached attention states for that shared prefix rather than recomputing it. This is especially valuable for RAG applications, chatbots with long system prompts, and few-shot classification workloads.

Is SGLang production-ready?

Yes. SGLang is used in production by researchers and companies serving Llama, Mistral, and other models. It provides an OpenAI-compatible API server, so existing applications can switch to SGLang with minimal code changes. The project is actively developed by the LMSYS team (creators of Vicuna and Chatbot Arena).

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Listed 2026-02-28 · updated 2026-11-12