LightLLM

LightLLM is a self-hosted LLM inference and serving framework for developers and teams running models on their own GPU servers. Its focus is serving requests efficiently: it schedules work around predicted memory use and handles text encoding, model inference and output decoding asynchronously to improve GPU utilization.

The framework is written in Python and licensed under Apache 2.0. Its extensible design suits teams that need to add model support or adapt a serving backend. Supported deployments include DeepSeek R1 and GLM-4.7-Flash, alongside multimodal models and reward models.

For applications connecting to the server, LightLLM provides an OpenAI-compatible API, function calling, structured output and reasoning parsers. These capabilities matter when model responses need to feed application logic rather than appear only as chat text. It also supports generating multiple results for a request.

On the server side, quantization in int8, fp8 and int4 helps teams manage model memory use. Tensor, data and expert parallelism provide ways to distribute inference work, while a dynamic KV cache manages memory used during generation. Memory-aware scheduling aims to reduce request eviction, and shared request objects reduce communication overhead between processes. LightLLM also supports multi-level caching and prefix KV cache transfer between data-parallel workers.

Install from source or the official Docker image on Linux. The documented NVIDIA setup requires GPU compute capability 7.0 or newer and sufficient shared memory for the selected workload.