SGLang - an open-source inference framework for large models, adept at handling repetitive prompt structures.

What is SGLang?

SGLang is an open-source, high-performance inference framework for large-scale language and multimodal models. The framework provides low-latency, high-throughput inference services, supporting deployments from single GPUs to large-scale distributed clusters. SGLang supports various models (such as Llama, Qwen, and DeepSeek) and hardware (including NVIDIA and AMD GPUs, CPUs, and TPUs). SGLang boasts active community support, continuously driving technological innovation and the establishment of industry standards, facilitating the efficient deployment of language models in practical applications.

SGLang's main functions

• High-performance inferenceSGLang provides low-latency, high-throughput inference services, supporting deployments from single GPUs to large-scale distributed clusters, and employs various optimization techniques to improve efficiency.
• Extensive model supportIt is compatible with a variety of large language models and multimodal models, such as Llama, Qwen, and DeepSeek, and supports Hugging Face models and OpenAI API, making it easy to extend to new models.
• Multi-hardware platform compatibilitySupports various hardware such as NVIDIA, AMD, Intel, and Google TPU, ensuring efficient operation on different platforms and providing flexible deployment options.
• Advanced optimization technologyThe framework employs optimization strategies such as prefix caching, decoding separation, speculative decoding, and pagination attention, and supports quantization techniques to further improve inference performance.

• Install SGLangInstall via pip (pip install --upgrade pip; pip install uv; uv pip install "sglang") or use Docker (docker pull sglang/sglang:latest; docker run -it sglang/sglang:latestEnsure that CUDA is configured before installation (if GPU acceleration is required).
• Start service:run sglang serve --model-path /path/to/your/model Start the service, specify the model path, and the service listens on port 8000 by default.
• Query API:use curl Or Python requests Ku Xiang http://localhost:8000/v1/chat/completions Send requests to interact with the model using a standard OpenAI-compatible format.
• Reference documentsVisit the official SGLang documentation for detailed configuration, model support, hardware optimization, and deployment guidelines.

SGLang's project address

• Project official websitehttps://www.sglang.io/
• GitHub repositoryhttps://github.com/sgl-project/sglang

Application scenarios of SGLang

• Large-scale language model inferenceSGLang is suitable for scenarios that require efficient processing of large amounts of text data, such as intelligent customer service, chatbots, and content generation. It can quickly respond to user requests and generate high-quality text content.
• Multimodal model deploymentThe framework can be used in creative design, advertising production, virtual reality and other fields to achieve rapid generation of text into images or videos.
• Enterprise applicationsIn enterprise environments, it is used for tasks such as data analysis, report generation, and intelligent recommendations to help improve work efficiency and decision-making quality.
• Academic research and developmentIt provides researchers with an efficient model inference platform that supports a variety of models and hardware, facilitating large-scale experiments and research.
• Cloud computing and edge computingIt can be deployed on cloud or edge devices, supporting flexible deployment from a single GPU to a large-scale distributed cluster to meet different computing needs.