What is this?
SGLang is a high-performance serving framework for large language models and multimodal models, built to deliver low-latency, high-throughput inference from a single GPU up to large distributed clusters.
SGLang is a high-performance serving framework for large language models and multimodal models, built to deliver low-latency, high-throughput inference from a single GPU up to large distributed clusters.Is this a model, or the engine that runs models?It is a serving framework/runtime (an inference engine) you deploy to serve existing models — it is not itself a model, and it is compatible with most Hugging Face models and OpenAI-style APIs.
It is a serving framework/runtime (an inference engine) you deploy to serve existing models — it is not itself a model, and it is compatible with most Hugging Face models and OpenAI-style APIs.How do I get it running?Follow the README's 'Install SGLang' and 'Quick Start' guides to install the package and launch a server for your chosen model.
Follow the README's 'Install SGLang' and 'Quick Start' guides to install the package and launch a server for your chosen model.What hardware do I need to run it?You need a supported accelerator — SGLang runs on NVIDIA GPUs (GB200/B300/H100/A100/Spark/5090), AMD GPUs (MI355/MI300), Intel Xeon CPUs, Google TPUs, or Ascend NPUs.
You need a supported accelerator — SGLang runs on NVIDIA GPUs (GB200/B300/H100/A100/Spark/5090), AMD GPUs (MI355/MI300), Intel Xeon CPUs, Google TPUs, or Ascend NPUs.Is this meant for a laptop, or production-scale deployments?It's built for serious serving workloads: it's used in production generating trillions of tokens daily and scales from one GPU to large multi-GPU clusters with disaggregated prefill/decode and expert parallelism, so getting the most out of it assumes real GPU infrastructure.
It's built for serious serving workloads: it's used in production generating trillions of tokens daily and scales from one GPU to large multi-GPU clusters with disaggregated prefill/decode and expert parallelism, so getting the most out of it assumes real GPU infrastructure.Who actually uses SGLang in production?It's adopted by major AI labs, cloud providers and research institutions — including xAI, NVIDIA, AMD, Microsoft Azure, AWS, LinkedIn, Cursor, Google Cloud, MIT, Stanford, Alibaba and Tencent — and is also used as a rollout backend for RL/post-training frameworks.
It's adopted by major AI labs, cloud providers and research institutions — including xAI, NVIDIA, AMD, Microsoft Azure, AWS, LinkedIn, Cursor, Google Cloud, MIT, Stanford, Alibaba and Tencent — and is also used as a rollout backend for RL/post-training frameworks.What makes SGLang fast compared to other LLM servers?Its core RadixAttention mechanism enables efficient prefix caching, combined with a zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention and multi-level parallelism (tensor/pipeline/expert/data).
Its core RadixAttention mechanism enables efficient prefix caching, combined with a zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention and multi-level parallelism (tensor/pipeline/expert/data).Which models can I serve with it?It supports a wide range of language models (Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral), embedding and reward models, and diffusion models like WAN and Qwen-Image, and is easily extensible to new models.
It supports a wide range of language models (Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral), embedding and reward models, and diffusion models like WAN and Qwen-Image, and is easily extensible to new models.Does SGLang do more than just inference serving?Yes — it's also used as a proven rollout backend for reinforcement learning and post-training, with native integrations adopted by frameworks like AReaL, Miles, slime, Tunix and verl.
Yes — it's also used as a proven rollout backend for reinforcement learning and post-training, with native integrations adopted by frameworks like AReaL, Miles, slime, Tunix and verl.What are similar projects?vllm, tokenspeed, LoongForge, TensorRT-LLM, ktransformers, FreeToken, project-maya, verl
vllm, tokenspeed, LoongForge, TensorRT-LLM, ktransformers, FreeToken, project-maya, verl