lightllm

LightLLM is a Python-based inference and serving framework for large language models. Its stated goals are a lightweight design, easy scalability and high-speed performance. The project says it draws on ideas from several open-source efforts, including FasterTransformer, Text Generation Inference (TGI), vLLM and FlashAttention. Documentation is available in English and Chinese, with a quick start, installation guide and deployment tutorials (for example DeepSeek deployment). The README points to release blogs for performance details rather than listing benchmark numbers. Notable project news includes a v1.1.0 release, a v1.0.0 release, support for prefix KV cache transfer between DP rankers, and papers around constrained decoding (Pre3, ACL 2025) and a request scheduler (ASPLOS 2025). The README states LightLLM's pure-Python design and token-level KV cache management make it convenient as a base for research. Several projects are listed as using LightLLM or its components, including LoongServe, vLLM, SGLang, ParrotServe, Aphrodite, S-LoRA, OmniKV and LazyLLM. Academic works cited as based on or using parts of LightLLM include ParrotServe, SLoRA, LoongServe, ByteDance's CXL, VTC, OmniKV, CaraServe, LoRATEE and FastSwitch. The repository is released under Apache-2.0. Community support is offered through a Discord server, and the README invites contributions and cooperation.