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SGLang

Fast serving framework for complex LLM workflows and multi-turn structured generation.

LicenseApache-2.0
GitHub stars8.5k
Last commit1 weeks ago
Tags6 topics
Ai MlLlm ServingCudaRadix AttentionStructured DecodingPython
Overview

Why consider SGLang?

SGLang is a fast serving framework for large language models and vision-language models. It accelerates complex LLM prompting with RadixAttention (prefix caching), structured JSON decoding, and speculative decoding.

Guided learning

Learn SGLang by building

Practical setup notes, real use cases, and copy-ready examples in one focused guide.

1 min read 2 sections
In this guide2 sections

Overview of SGLang

SGLang delivers up to 5x higher throughput for agent workflows and chain-of-thought pipelines by intelligently reusing KV-caches across multi-turn requests.

Quickstart

bash
1pip install "sglang[all]"
2python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000

SGLang is licensed under the Apache License Version 2.0.

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SGLang FAQs

SGLang is listed as a Ai Ml tool on TiloBox. Review the overview, features, and official documentation on this page to decide whether it solves your specific workflow.

Start with the project's GitHub repository and official website for supported installation and deployment instructions. Test the setup with representative data or a small project before rolling it out more widely.

SGLang is listed under the Apache-2.0 license. Read the complete license text and the project's notices before using, modifying, or distributing the software.

Production readiness depends on your requirements. Review maintenance activity, security practices, documentation, backup and upgrade procedures, and compatibility with your stack; then validate it in a non-production environment.

SGLang is listed as an alternative to TensorRT-LLM. Compare the core workflow, deployment model, integrations, and licensing against your must-have requirements before switching.