vLLM

vLLM is an open-source LLM inference engine released by UC Berkeley researchers in 2023 that has become the dominant high-throughput serving framework for self-hosted LLM deployments. The framework's key innovation is PagedAttention, a memory management algorithm inspired by virtual memory in operating systems that organizes the KV cache into fixed-size blocks, dramatically reducing memory fragmentation and enabling 2x-24x throughput improvements over naive implementations. vLLM also implements continuous batching, where new requests join a running batch without waiting for the current batch to complete, maintaining high GPU utilization across varying request lengths. The framework supports hundreds of LLM architectures including Llama, Mistral, Qwen, Mixtral, Gemma, DeepSeek, and OpenAI-compatible API endpoints. vLLM is widely deployed by companies hosting their own LLM infrastructure, including AnyScale, Lambda Labs, RunPod, and Together AI. AI governance teams adopt vLLM for self-hosted deployments where data must not leave the enterprise boundary, pairing it with Centralpoint-style governance for token metering and audit logging.

vLLM-hosted models in Centralpoint: Centralpoint sits in front of vLLM endpoints alongside cloud APIs in one model-agnostic platform. The platform meters tokens per skill and audience, keeps prompts local, supports both generative and embedded models, and deploys self-hosted-LLM chatbots through one line of JavaScript on any portal.


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