SGLang: A High-Performance Large Model Serving Framework

SGLang is a high-performance inference serving framework designed specifically for large language models and multimodal models, aiming to provide efficient model deployment and serving capabilities.

SGLang is a high-performance serving framework for large language models and multimodal models.

• High-performance inference: Optimized for large model inference
• Multimodal support: Supports both language models and multimodal models
• Production-grade deployment: Provides stable serving capabilities

This project focuses on addressing key challenges in large model deployment:

• Inference throughput optimization
• Latency reduction
• Efficient resource utilization

https://github.com/sgl-project/sglang

Suitable for developers and enterprises that need to build their own large model inference services.