Glossary

The AI compute terms new developers search before they trust the route

Use this glossary to learn the key ideas behind AI compute without getting pushed straight into hardware hype. Each term is defined in plain language, then tied back to workload execution and what matters in practice.

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Best for
Beginner definitions
Good for first-time builders who need vocabulary before deeper guide pages.
Focus
Workload-first meanings
Terms are explained through what they mean for execution, not just raw hardware.
Next move
Branch into guides
Use the term pages as bridges into planning, pricing, and product pages.

Why this glossary exists

Definitions should reduce confusion, not create a shopping list

Many AI compute definitions start with hardware jargon before explaining why the term matters. Here, each definition begins in plain language and connects the concept to a real AI workload.

Once the concept is clear, you can make better decisions about workload fit, cost, and execution without turning every question into hardware shopping.

Related pages

Glossary terms

Start with the concept you are stuck on, then follow the related links into guides and product pages.

what is a gpuGPUA GPU is a processor designed for highly parallel computation. In AI, GPUs matter because they make model inference and training much faster than general-purpose CPUs for many workloads.what is vramVRAMVRAM is the memory attached to a GPU. In AI workloads, VRAM matters because it affects whether a model can fit and run cleanly on a given route.what is latencyLatencyLatency is the time it takes for a system to respond to a request. In AI apps, latency matters because it shapes whether the user experience feels instant, acceptable, or frustrating.