SGLang
Fast serving framework for large language models with structured generation and multi-call optimization
SGLang
Fast serving framework for large language models with structured generation and multi-call optimization
SGLang is a fast serving framework for large language models developed at UC Berkeley that optimizes complex LLM programs through RadixAttention and other techniques. It supports structured output generation (JSON schema, regex constraints) with minimal overhead and efficiently handles multi-call LLM programs that chain multiple model invocations. SGLang achieves significantly higher throughput than standard serving frameworks for programs involving multiple sequential model calls. AI researchers and production inference teams use SGLang when deploying complex multi-step LLM applications that require both high throughput and structured output guarantees. The framework integrates with popular open-source models and supports multi-GPU and multi-node deployment.
Key Features
- ✓RadixAttention optimization
- ✓Structured generation
- ✓Multi-call optimization
- ✓High throughput
- ✓Open-source
Quick Info
- Category
- AI Infrastructure
- Pricing
- Free
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