SGLang is a serving framework designed for low-latency, high-throughput inference of large language and multimodal models across diverse hardware. It provides a fast runtime with RadixAttention, continuous batching, and distributed parallelism for AI engineers and operations teams building model APIs.
Project overview
The framework delivers performance optimizations for inference and serving, including RadixAttention for prefix caching, a zero-overhead scheduler, and speculative decoding.
Project type
Model Development · Model Runtime · Infrastructure
Use cases
Image Processing
Deployment
Refer to project documentation
License
Apache-2.0
Best for
AI engineers and operations teams building serving infrastructure for large language and multimodal models.
Teams running reinforcement learning post-training workflows that require a rollout backend.
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/data parallelism.
Limitations and risks
GPU availability is documented as optional, but the minimum hardware requirements for specific deployment targets are not documented.
Cost dependencies, telemetry practices, and coding requirements are not documented in the available facts.
Getting started
Setup requires installing the framework via pip or from source, followed by configuring model parameters and hardware setups. The documented first success path includes installing SGLang and following 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…