# SGLang SGLang is an open-source serving framework for large language and multimodal models. It helps developers deploy model servers and query them through OpenAI-compatible endpoints across supported GPU, CPU, TPU, NPU, and XPU platforms. - Findings24: - 网站: - 价格: Freemium - 分类: AI 推理平台, AI 模型部署 ## 什么是 SGLang？ SGLang is an open-source inference and serving framework for large language and multimodal models. Its core purpose is to run model servers for production workloads, with support for deployments ranging from a single GPU to distributed clusters. The framework combines model and hardware flexibility with serving optimizations intended to improve latency and throughput. It supports language, vision-language, diffusion, and other multimodal model workloads on platforms including NVIDIA and AMD GPUs, CPU servers, TPUs, Ascend NPUs, and XPUs. A typical workflow is to install SGLang through pip or Docker, start a server for a selected model, and query it through OpenAI-compatible endpoints. ## SGLang 能做什么？ ### Production-oriented model serving Runs inference workloads from a single GPU to distributed clusters, with the site positioning the framework for low-latency and high-throughput deployments. ### Broad model coverage Supports open model families including DeepSeek, Qwen, Llama, Mistral, and GLM, as well as vision-language, diffusion, OCR, speech, and other multimodal models listed in the model catalog. ### Cross-platform hardware support The supported hardware list includes NVIDIA GPUs, AMD GPUs, CPU servers, Google TPUs, Ascend NPUs, and XPUs. ### Serving optimizations Includes disaggregated prefill and decode, speculative decoding, multiple parallelism strategies, a zero-overhead scheduler, and optimized GPU kernels. ### OpenAI-compatible API access After a server is launched, applications can interact with the selected model through standard OpenAI-compatible endpoints. ### Caching and operational ecosystem The project includes radix-tree caching capabilities and related ecosystem projects such as a Kubernetes operator for serving, GPU scheduling, and model lifecycle management. ## 使用场景 ### Deploying open models for applications Developers can install the framework, launch a server against a supported language or multimodal model, and connect an application through OpenAI-compatible API endpoints. ### Serving long-context and agentic workloads Teams evaluating supported reasoning, coding, and agent-oriented models can use the serving engine and its parallelism, caching, and decoding optimizations as part of a model-serving stack. ### Multimodal and generative model inference The model catalog covers vision-language, OCR, image, video, audio, and diffusion models, making the framework applicable to supported non-text inference workloads as well as standard LLM serving. ### Distributed infrastructure operations Organizations running model services across clusters can use the framework's distributed serving capabilities and explore the surrounding ecosystem, including OME, a Kubernetes operator for LLM serving, GPU scheduling, and model lifecycle management. ### Research and reinforcement-learning systems Related projects in the ecosystem support speculative-decoding draft-model training, reinforcement-learning data generation, and large-scale MoE post-training workflows built around SGLang. ## 常见问题 ### How do you get started with SGLang? The site describes a three-step workflow: install SGLang with pip or Docker, launch the server with a selected model, and query it through an OpenAI-compatible API. Platform-specific package commands depend on the chosen runtime and hardware. ### What types of models does SGLang support? The catalog includes LLMs, mixture-of-experts models, vision-language and other multimodal models, diffusion models, OCR models, and speech-related models. Examples of listed model families include DeepSeek, Qwen, Llama, Mistral, and GLM. ### Which hardware platforms are supported? The site lists NVIDIA GPUs, AMD GPUs, CPU servers, TPUs, Ascend NPUs, and XPUs. Actual compatibility depends on the model, backend, and platform-specific support. ### Can applications use an OpenAI-style interface? Yes. The documented basic workflow uses standard OpenAI-compatible endpoints after the SGLang server is launched. ### Is SGLang open source? Yes. The site presents SGLang as an open-source serving framework and links to its codebase and community resources. ## 快速信息 - **Category**: Developer Tool; AI Infrastructure - **Primary function**: Serving inference for LLM and multimodal models - **Deployment range**: Single GPU to distributed clusters - **API style**: OpenAI-compatible endpoints - **Supported hardware**: NVIDIA GPUs, AMD GPUs, CPUs, TPUs, Ascend NPUs, and XPUs - **Installation options**: pip or Docker