vLLM

vLLM is an open-source LLM inference engine developed at UC Berkeley that became one of the most popular high-throughput serving systems for large language models. Its hallmark innovation is PagedAttention — a memory management technique modeled on virtual memory in operating systems that dramatically improves KV cache utilization. The result is 14-24x throughput improvements over earlier serving systems on common workloads. vLLM supports continuous batching, prefix caching, speculative decoding, tensor parallelism, pipeline parallelism, and many quantization formats. It powers production deployments at major companies and is the de facto standard for self-hosted LLM serving. Compatible with OpenAI's API schema, it allows applications written for OpenAI to be redirected to self-hosted Llama, Mistral, Qwen, or DeepSeek models with minimal code changes. AI governance, AI compliance, and AI risk management programs document vLLM versions and configurations in deployment records as part of responsible AI evidence across self-hosted enterprise AI environments.

Centralpoint Pairs With vLLM for Sovereign AI Deployments: Run vLLM-hosted Llama or Mistral behind Centralpoint by Oxcyon for full on-prem control. The model-agnostic platform also supports OpenAI, Gemini, and other clouds. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds chatbots into your portals via one line of JavaScript.


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