SGLang
技能 已验证 活跃Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
To provide a fast, efficient, and versatile solution for serving LLMs, enabling structured output generation and accelerating AI agent workflows through advanced caching.
功能
- Fast LLM serving with RadixAttention
- Automatic prefix caching for agents and few-shot learning
- Structured generation (JSON, regex, grammar)
- OpenAI-compatible API endpoint
- Support for multiple GPU vendors and quantization
使用场景
- Accelerating agentic workflows with repeated prompts
- Enabling fast, structured JSON/regex output for LLMs
- Deploying LLMs at scale with optimized inference
- Serving multimodal models with image inputs
非目标
- Providing a framework for fine-tuning LLMs
- Replacing general-purpose LLM libraries for simple text generation without caching benefits
- Acting as a data processing pipeline for training datasets
安装
请先添加 Marketplace
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skills质量评分
已验证类似扩展
Sglang
75Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
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Slack
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Github
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