Detailed Introduction
Mini-SGLang is a lightweight yet engineering-focused high-performance inference framework for large language models. It aims to simplify complex inference systems into a readable and extensible codebase. The project supports local deployment and online serving, exposes an OpenAI-compatible API, and includes interactive shells, online server modes, and multiple examples to help developers get started rapidly.
Main Features
- High performance: Optimizations include radix cache for prefix reuse, chunked prefill to reduce peak memory, overlap scheduling to hide CPU overhead, tensor parallelism for multi-GPU scaling, and integration with high-performance kernels such as FlashAttention.
- Lightweight & readable: A compact ~5k lines of Python with modular structure and type annotations, designed for transparency and modification.
- Multi-scenario deployment: Support for local GPU-based serving (CUDA required) and online services, with examples for code interpreter, browser automation, and filesystem operations.
Use Cases
- Large-scale online inference and batch testing in controlled environments.
- Research and engineering reference to validate inference optimization strategies and performance benchmarks.
- Quickly deploy an OpenAI-compatible inference endpoint for development and testing.
Technical Features
- OpenAPI / compatible interfaces: Provides standard service APIs for easy client integration.
- Optimized kernels: Integrates FlashAttention/FlashInfer and other optimized operators to boost single-GPU performance.
- Extensible architecture: Modular components (executor, scheduler, cache, communication) enable custom distributed and parallel strategies.