SGLang: An Open-Source Inference Engine Transforming LLM Deployment through CPU Scheduling, Cache-Aware Load Balancing, and Rapid Structured Output Generation

SGLang is an open-source inference engine designed to tackle computational challenges when deploying large language models (LLMs). It optimizes the balance between CPU and GPU resources for higher efficiency and throughput. Innovative features include RadixAttention for minimizing redundant computations, a zero-overhead batch scheduler to ensure continuous processing, and a cache-aware load balancer for better resource utilization. Additionally, SGLang excels in generating structured outputs like JSON rapidly and efficiently. Its practical benefits are demonstrated by its adoption in large-scale production environments, resulting in significant cost savings and performance improvements.