The news seems small: a glossary is out to help navigate the flood of artificial intelligence terms. Yet the announcement alone signals a problem that those in the field know well: AI has become a jungle of words. Large Language Models, fine-tuning, quantization, VRAM, inference — the barrier to entry is no longer just computational, but also lexical. For a company evaluating whether to bring models onto its own servers, mixing up one term with another can cost months of work and misguided choices.

The glossary promises to bring order. In a landscape where every vendor uses its own vocabulary, a shared reference is the first step toward informed decisions. Take on-premise deployment: here we talk about self-hosted, about local stacks that never leave the corporate perimeter. The reasons may be data sovereignty, latency, cost predictability. But to assess such a project, one must confidently handle notions like VRAM, throughput in tokens per second, quantization to INT8 or FP16. Without a map, you risk comparing incomparable solutions.

It’s not just a hardware matter. The glossary indirectly touches on a theme dear to AI‑RADAR: the difference between cloud and local isn’t only about initial investments. Those who choose self-hosted must understand that fine-tuning requires dedicated resources, that inference on consumer GPUs can be a trade-off, and that the word “privacy” changes meaning when data never leaves the rack. A glossary doesn’t solve these dilemmas, but it supplies the words to tackle them without ambiguity.

Hence the importance of distinguishing, say, between an LLM and a simple statistical model, or between operational costs (OpEx) and investments (CapEx) when calculating TCO. Regulatory compliance, too, hinges on linguistic precision: GDPR doesn’t apply to cloud the same way it does to bare metal, but if the terms get confused, so do the responsibilities.

Ultimately, the terminological noise is the symptom of an explosive growth phase. Every day a new framework emerges, a new quantization approach, a new benchmark. Having a glossary isn’t just convenient: it’s a defense tool against commercial rhetoric. When a vendor promises “inference ready in 5 minutes,” knowing what inference means and which constraints it entails helps separate reality from marketing. For those building on-premise AI infrastructure, clarity of words is the first form of control.