SGLang
Open-sourceHigh-performance serving framework for large language models and multimodal models with optimized inference.
About SGLang
SGLang is a high-performance serving framework for large language models and multimodal models. Written in Python, it provides optimized inference with support for various architectures including transformers, achieving efficient LLM deployment with 26.3k GitHub stars.
Best For
- Deploying LLMs at scale with high throughput
- Production AI inference infrastructure
Pros & Cons
Pros
- + Optimized for high-throughput model serving
- + Supports both LLMs and multimodal models
- + Active development with regular performance improvements
Cons
- - Requires technical knowledge for deployment and tuning
- - Focused on serving rather than training
Pricing
Open source and free to use
Key Features
- High-performance LLM serving with optimized inference
- Support for both language and multimodal models
- Python implementation for easy integration
- Open source with 26.3k GitHub stars
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