vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs) developed by UC Berkeley's Sky Computing Lab.

With its revolutionary PagedAttention algorithm, vLLM achieves 14-24x higher throughput than traditional serving methods, making it the go-to choice for production LLM deployments.




What...