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Scripts2026年4月1日·1 分钟阅读

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

快速使用

先拿来用，再决定要不要深挖

这里应该同时让用户和 Agent 知道第一步该复制什么、安装什么、落到哪里。

# 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!"}]
}'

介绍

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>.

🙏

来源与感谢

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

SGLang — Fast LLM Serving with RadixAttention

先拿来用，再决定要不要深挖

Key Features

FAQ

来源与感谢

相关资产

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