SGLang

SGLang (Structured Generation Language) is a high-performance open-source inference framework for large language models (LLMs) and vision-language models, developed by the LMSYS research group at UC Berkeley. It pairs a Python-embedded frontend language—offering primitives for chained generation, parallelism, and constrained decoding—with a highly optimized runtime backend featuring RadixAttention for automatic KV cache reuse. Now part of the PyTorch ecosystem, SGLang powers trillions of tokens daily across deployments at xAI, NVIDIA, AMD, Google Cloud, Microsoft Azure, and AWS.

As LLM serving costs become a critical bottleneck for AI products, SGLang's RadixAttention, continuous batching, and kernel fusion deliver substantial throughput gains over alternatives like vLLM, making it a preferred engine for high-scale production deployments. Companies building agentic systems, RAG pipelines, and multi-step reasoning workflows increasingly rely on SGLang's prefix-caching and structured-output capabilities to keep latency and cost under control. Engineers who can tune, deploy, and optimize SGLang clusters are in growing demand as model serving moves from proof-of-concept to production infrastructure.