SGLang
Fast serving framework for large language models and vision-language models, with a structured generation language for complex LLM programs.
- Actively maintained
- Well documented
- Permissive license
Overview
SGLang combines a high-performance inference backend with a frontend language for expressing complex LLM interactions - multi-step reasoning, structured output, tool calls - as a single program the runtime can optimize as a whole, rather than as a series of separate API calls. It targets both raw serving throughput and expressiveness for complex agent-style workloads.
SGLang is a fit for teams that need high-throughput LLM serving combined with structured, multi-step generation patterns, a performance-focused alternative to vLLM (also in this catalog) that teams commonly benchmark against each other.
Why it's in this catalog
Catalog rating: ★★★★☆ (4/5)
Fast-growing high-performance LLM serving engine, a strong alternative to vLLM.
- Mature
- Community-driven
- Categories
- AI & Machine Learning
- Languages
- Python
- License
- Apache-2.0
Related Projects
vLLM
Apache-2.0High-throughput, memory-efficient inference and serving engine for large language models, built for running LLMs in production at scale rather than on a single local machine.
Actively maintained
- AI & Machine Learning
llama.cpp
MITHigh-performance C/C++ implementation for running LLM inference locally on consumer hardware, including CPUs, with minimal dependencies.
Actively maintained
- AI & Machine Learning
KServe
Apache-2.0Kubernetes-native platform for serving machine learning models at scale, standardizing model deployment across frameworks.
Actively maintained
- AI & Machine Learning
Milvus
Apache-2.0Open source vector database built for scalable similarity search and AI applications.
Actively maintained
- AI & Machine Learning
- Database
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