SGLang Documentation ==================================== SGLang is a fast serving framework for large language models and vision language models. It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language. The core features include: - **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ). - **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions. - **Extensive Model Support**: Supports a wide range of generative models (Llama 3, Gemma 2, Mistral, QWen, DeepSeek, LLaVA, etc.) and embedding models (e5-mistral), with easy extensibility for integrating new models. - **Active Community**: SGLang is open-source and backed by an active community with industry adoption. .. toctree:: :maxdepth: 1 :caption: Getting Started install.md backend.md frontend.md .. toctree:: :maxdepth: 1 :caption: References sampling_params.md hyperparameter_tuning.md model_support.md contributor_guide.md choices_methods.md benchmark_and_profiling.md troubleshooting.md