sgl-project/sglang

Project overview

SGLang is a serving framework for large language and multimodal models, providing runtime features such as RadixAttention and tensor parallelism across diverse hardware. It is designed for teams that need low-latency, high-throughput inference and supports integrations with OpenAI APIs and Hugging Face models.

Project type

Model Development · Model Runtime · Infrastructure

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• AI engineers, operations teams, and developers who need to serve large language and multimodal models with high throughput and low latency on distributed hardware.
• Teams building reinforcement learning pipelines that require a proven rollout backend for training frontier models.

Key capabilities

• Provides efficient serving with features like RadixAttention for prefix caching, a zero-overhead scheduler, speculative decoding, continuous batching, and paged attention.
• Supports a wide range of language, embedding, reward, and diffusion models with compatibility for most Hugging Face models and OpenAI APIs.
• Runs on NVIDIA GPUs, AMD GPUs, Intel Xeon CPUs, Google TPUs, and Ascend NPUs.
• Serves as a proven rollout backend used for training many frontier models with native RL integrations.
• Enables low-latency and high-throughput inference across large distributed clusters using tensor, pipeline, expert, and data parallelism.

Limitations and risks

• Setup is rated medium difficulty due to the need for configuring model parameters and hardware environments.
• While GPUs are optional, minimum hardware specifications are not documented and require manual setup and verification.

Getting started

• Requires installing the framework via pip or source and configuring model parameters and hardware setups. First success path involves installing SGLang and using the Quick Start guide.

Evidence and sources

• GitHub project description: SGLang is a high-performance serving framework for large language models and multimodal models.
• README: SGLang is a high-performance serving framework for large language models and multimodal models. It is designed to deliver low-latency and high-throughput inference across a wide r…
• README: SGLang has been deployed at large scale, generating trillions of tokens in production each day. It is trusted and adopted by a wide range of leading enterprises and institutions,…
• README: As an open-source LLM inference engine, SGLang has become the de facto industry standard, with deployments running on over 400,000 GPUs worldwide.
• README: **RL & Post-Training Backbone**: SGLang is a proven rollout backend used for training many frontier models, with native RL integrations and adoption by well-known post-training fr…

Reviewed at: 2026-07-17T10:00:18Z