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SGLang
High‑performance serving framework for LLMs and vision‑language models.
SGLang provides low‑latency, high‑throughput inference for large language and vision‑language models, scaling from a single GPU to distributed clusters with extensive hardware and model compatibility.
Overview
Overview
SGLang is a high‑performance serving engine designed for developers, enterprises, and research labs that need fast, scalable inference of large language models (LLMs) and vision‑language models (VLMs). It delivers low latency and high throughput across a spectrum of deployments, from a single GPU workstation to massive multi‑node clusters.
Capabilities & Deployment
The framework features RadixAttention prefix caching, a zero‑overhead CPU scheduler, speculative decoding, continuous batching, and support for quantization (FP4/FP8/INT4) and multi‑LoRA batching. It runs on NVIDIA, AMD, Intel, Google TPU, and Ascend hardware, and integrates seamlessly with Hugging Face and OpenAI‑compatible APIs. SGLang’s flexible Python frontend enables chained generation calls, advanced prompting, and multimodal inputs, while its active community and extensive documentation accelerate adoption in production environments.
Adoption
Deployed on over 300,000 GPUs worldwide, SGLang powers token generation for leading cloud providers, universities, and AI‑focused enterprises, establishing it as a de‑facto standard for LLM inference.
Highlights
RadixAttention prefix caching and speculative decoding for ultra‑low latency
Zero‑overhead CPU scheduler with continuous batching and expert parallelism
Broad hardware support: NVIDIA, AMD, Intel, TPU, Ascend, and more
Extensible Python frontend for multimodal prompts, LoRA batching, and custom models
Pros
- Delivers industry‑leading inference speed and throughput
- Supports a wide range of LLM and VLM architectures
- Scales from single‑GPU to large distributed clusters
- Active open‑source community with extensive documentation
Considerations
- Steep learning curve for advanced parallelism features
- Requires GPU or accelerator resources for optimal performance
- Primarily Python‑centric, limiting non‑Python integrations
- Custom model integration may need additional engineering effort
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Fit guide
Great for
- Enterprises building high‑scale AI APIs or chat services
- Developers creating multimodal applications with LLMs and VLMs
- Research labs needing fast inference for large model experiments
- Cloud providers offering low‑latency AI inference as a service
Not ideal when
- Edge devices with minimal compute or memory
- Teams without Python development expertise
- Workloads focused on model training rather than inference
- Users seeking a ready‑made graphical UI for model serving
How teams use it
Real‑time conversational AI
Provides sub‑100 ms response times for chatbots handling millions of concurrent users.
Multimodal content generation
Enables seamless text‑and‑image generation pipelines for marketing and creative workflows.
High‑throughput embedding service
Processes billions of embedding requests daily for search and recommendation systems.
Large‑scale token generation for research
Accelerates massive language model pre‑training data synthesis across distributed GPU clusters.
Tech snapshot
Python75%
Rust14%
Cuda6%
C++4%
Shell1%
C1%
Frequently asked questions
What license does SGLang use?
SGLang is released under the Apache‑2.0 license.
Which programming language is required to use SGLang?
The primary interface is Python, with core runtime components in C/C++ and CUDA.
Can SGLang run on AMD GPUs?
Yes, it supports AMD Instinct MI300X, MI355, and other AMD accelerators.
Is SGLang compatible with Hugging Face models?
SGLang works with most Hugging Face model formats and OpenAI‑compatible APIs.
Where can I find community support?
Join the project’s Slack, attend bi‑weekly development meetings, and consult the documentation and blog posts.
Project at a glance
Active- Stars
- 22,618
- Watchers
- 22,618
- Forks
- 4,129
LicenseApache-2.0
Repo age2 years old
Last commit12 hours ago
Primary languagePython
Last synced 12 hours ago