Understand it in 10 seconds
Inference is the stage where a trained model processes new input and produces an output.
In plain language
Inference is the stage where a trained model processes new input and produces an output. This describes what the model or system does, not a promise that every product behaves identically.
An analogy
Training is practice; inference is using the trained model on a new request.
The analogy is a shortcut, not a complete technical definition.
An everyday example
Where you will encounter it
It appears in discussions of API cost, latency, hardware, deployment and local models.
Should you care?
Users mainly notice speed, cost and output quality. Model operators need the technical details.
How it differs
Training updates model parameters. Inference uses trained parameters to produce an output.
Related Terms
Related terms
Continue with these published explanations.
Sources & last checked
Checked against official documentation. Editorial recommendations are distinguished from vendor positioning; no runtime benchmark was performed.
Official documentation
01Last checked: September 5, 2026
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