vLLM: complete definition in AI for SMEs

vLLM is an open-source inference engine optimized for serving language models in production, with significantly better memory efficiency and throughput than alternatives. It uses a technique called PagedAttention that manages GPU memory like an operating system manages RAM — allowing more simultaneous requests with fewer hardware resources.

What it changes for an SME

vLLM matters when an SME wants to run models internally (on-premise or private cloud) rather than sending data to an external API:

• Confidentiality: customer data, contracts and sensitive documents never leave your infrastructure;
• Controlled cost: a dedicated GPU server can serve an open-source model (Llama, Mistral) at a fixed monthly cost, lower than a pay-per-use API at high volume;
• Latency: no internet transit, local response in hundreds of milliseconds.

What you should know

vLLM is a technical tool — it requires a GPU server, Linux skills and manual installation. It is not a one-click installer. For an SME, the question is not whether you install it yourself, but knowing this option exists and can be more economical and secure than an external API. An AI audit determines whether the on-premise model fits your use case, and fractional AI leadership manages the deployment.