Introduction
SGLang is a high-performance inference and serving framework for large language models and vision language models. It supports multimodal models, extreme concurrency, and flexible frontend programming, widely adopted in enterprise production environments.
Key Features
- Efficient backend inference with RadixAttention, zero-overhead scheduling, distributed parallelism
- Flexible frontend language for chained generation, control flow, multimodal input, and external interaction
- Supports mainstream LLMs, embedding models, and reward models, easily extensible
- Active open-source community, widely adopted in industry
Use Cases
- Enterprise-scale LLM/VLM inference and deployment
- Multimodal AI application development
- High-concurrency production inference
- Rapid prototyping and integration for LLM applications
Technical Highlights
- Python/Rust/C++/CUDA multi-language collaboration, extreme performance optimization
- Supports GPU/CPU hybrid inference and distributed deployment
- Built-in quantization, caching, structured output, and other advanced features