A Large Language Model (LLM) is an AI model trained on a massive volume of text — books, articles, code, conversations — to learn how to understand and generate natural language. It's the technology behind AI assistants like ChatGPT, Claude, and the internal assistants many businesses are now deploying: an LLM reads a question, "reasons" through billions of parameters, and returns a contextually relevant answer. But having a powerful model is only the first step — the bigger challenge for most businesses is running that model efficiently, reliably, and at a predictable cost.

Running an LLM (called inference) costs far more than most people expect. A large model needs tens of gigabytes of GPU memory just to load its parameters, and when many users send requests at once, the system has to process them in parallel without slowing down or running out of memory. If the inference infrastructure isn't optimized, a business ends up buying extra GPUs just to compensate for slow processing and request queues — a hidden cost few account for before deployment.

vLLM is an open-source inference-serving engine, born from research at UC Berkeley, built specifically to solve that problem. Its core technique — PagedAttention — manages a model's "context memory" (the KV cache) in small pages, the same way an operating system manages computer memory, instead of allocating one wasteful contiguous block per request. The result: the same GPU can serve many more concurrent users, without a hardware upgrade.

Beyond smart memory management, vLLM also supports continuous batching — flexibly merging requests from many different users into the same GPU processing pass, instead of handling each question one at a time. For an internal AI assistant serving dozens of employees at once, the difference between having this optimization and not can mean a few seconds of response time versus tens of seconds — which often decides whether employees actually use the tool every day.

This is exactly the infrastructure layer DABO handles on the customer's behalf. When deploying Local AI or an internal knowledge assistant, DABO pre-configures an inference engine (applying techniques like vLLM) tuned to each business's actual hardware and usage load — so customers get the best possible response speed with the minimum GPU footprint needed, instead of having to research and run their own AI infrastructure team.