Inference, explained
Inference is the process of running a trained AI model on a new input to produce an output — it's what's happening computationally every time you send a prompt and receive a response.
AI development has two distinct phases: training (where the model learns from data) and inference (where the trained model is actually used). Training happens once, or periodically, and is extremely compute-intensive. Inference happens every time someone uses the model — every prompt you send triggers one inference run. When people talk about 'running a model,' they almost always mean inference.
Inference cost and speed are central concerns for anyone building AI-powered products. A single inference call might take milliseconds or several seconds depending on model size, response length, and available hardware. At scale, inference costs add up fast: a product with millions of daily users might run tens of millions of inference calls per day.
Hardware designed specifically for inference — like NVIDIA's inference-optimized GPUs or custom chips from Google (TPUs) and Amazon (Inferentia) — has become a major focus of the AI infrastructure industry. For most developers using AI via API, inference is simply the API call; the underlying hardware is abstracted away. Understanding the term helps you make sense of pricing models (which are usually per-token costs that reflect inference compute) and latency tradeoffs.
Go deeper
Wield's AI Foundations track covers this hands-on, in plain English, with real examples and a copy-paste prompt to try it yourself.
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