vLLM

vLLM is a high-throughput, memory-efficient inference engine for serving large language models. Developed at UC Berkeley, it uses PagedAttention to dramatically reduce memory waste and increase serving speed, making it one of the fastest open-source LLM serving frameworks available. vLLM supports a wide range of models and is widely deployed in production environments that need to serve LLMs at scale.

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