Is SGLang — Fast LLM Serving with RadixAttention free to use?

Yes. SGLang — Fast LLM Serving with RadixAttention is freely available on TokRepo. Check the Source & Thanks section on the asset page for the specific open-source license.

How do I install SGLang — Fast LLM Serving with RadixAttention?

Visit the asset page on TokRepo and click "Copy for agent" to get the installation instructions. Most assets can be installed with a single command.

ScriptsApr 1, 2026·2 min read

SGLang — Fast LLM Serving with RadixAttention

Name: SGLang — Fast LLM Serving with RadixAttention
Author: TokRepo精选

SGLang is a high-performance serving framework for LLMs and multimodal models. 25.3K+ GitHub stars. RadixAttention prefix caching, speculative decoding, structured outputs. NVIDIA/AMD/Intel/TPU. Apach

TokRepo精选 · Community

Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

# Install
pip install sglang[all]

# Launch server
python -m sglang.launch_server --model meta-llama/Llama-3.1-8B-Instruct --port 30000

# Query (OpenAI-compatible)
curl http://localhost:30000/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "meta-llama/Llama-3.1-8B-Instruct",
  "messages": [{"role": "user", "content": "Hello!"}]
}'

Intro

SGLang is a high-performance serving framework for large language models and multimodal models, delivering low-latency and high-throughput inference. With 25,300+ GitHub stars and Apache 2.0 license, SGLang features RadixAttention for efficient prefix caching, zero-overhead scheduling, prefill-decode disaggregation, speculative decoding, and structured output generation. It supports NVIDIA, AMD, Intel, Google TPU, and Ascend NPU hardware, with broad model compatibility including Llama, Qwen, DeepSeek, and diffusion models.

Best for: Teams deploying LLMs in production needing maximum throughput and lowest latency Works with: Claude Code, OpenAI Codex, Cursor, Gemini CLI, Windsurf Hardware: NVIDIA, AMD, Intel, Google TPU, Ascend NPU

Key Features

RadixAttention: Automatic prefix caching for repeated prompts
Zero-overhead scheduling: Minimal dispatch latency between requests
Speculative decoding: Faster generation with draft models
Structured outputs: JSON schema-constrained generation
Multi-hardware: NVIDIA, AMD, Intel, TPU, Ascend NPU
Expert parallelism: Efficient MoE model serving
OpenAI-compatible API: Drop-in replacement server

FAQ

Q: What is SGLang? A: SGLang is an LLM serving framework with 25.3K+ stars featuring RadixAttention prefix caching, speculative decoding, and multi-hardware support. OpenAI-compatible API. Apache 2.0.

Q: How do I install SGLang? A: Run pip install sglang[all]. Launch with python -m sglang.launch_server --model <model-name>.

🙏

Source & Thanks

Created by SGLang Project. Licensed under Apache 2.0. sgl-project/sglang — 25,300+ GitHub stars

◈Home 🏆Trending 👤Me

SGLang — Fast LLM Serving with RadixAttention

Use it first, then decide how deep to go

Key Features

FAQ

Source & Thanks

Related Assets

Windmill — Open-Source Internal Tool Platform

Agno — Production AI Agent Runtime

Semantic Kernel — Microsoft AI Agent Framework