LightLLM is a Python-based large language model inference and serving framework known for its lightweight design, easy scalability and high-speed performance. Developed by ModelTC, it borrows the best ideas from systems such as FasterTransformer, TGI, vLLM and FlashAttention, implemented in a clean, hackable pure-Python architecture.
LightLLM’s token-level KV cache management and modular design make it both fast in production and approachable as a research platform. Its performance work is backed by peer-reviewed research: its Past-Future request scheduler was published at ASPLOS 2025, and its Pre³ constrained-decoding work won an outstanding paper award at ACL 2025. The v1.0 release reported the fastest DeepSeek-R1 serving performance on a single H200 machine at the time. Notably, components of LightLLM have been adopted upstream — parts of its kernels are used by vLLM and SGLang — and it underpins or informs systems such as Microsoft’s ParrotServe, S-LoRA and Peking University’s LoongServe. Documentation is published in English and Chinese, with Docker images provided for deployment. It supports a wide range of model architectures, including mixture-of-experts models, and exposes an API suitable for production serving behind standard load balancers. LightLLM is Apache-2.0 licensed, has more than 4,300 stars on GitHub, and was under active development in 2026.
Free. LightLLM is open source under the Apache-2.0 license and runs on your own GPU infrastructure.
Infrastructure engineers serving LLMs at scale who want a lightweight, extensible alternative to heavier serving stacks, and systems researchers who need a readable codebase to prototype scheduling, caching and decoding ideas.