Using a trained machine learning model to make predictions or generate outputs on new data.
Machine learning has two main phases. Training is when a model learns from data, which can take days or weeks on powerful hardware. Inference is when the finished model is put to work: classifying a new photo, translating a sentence, or answering a chatbot question.
Inference happens far more often than training, so its speed and cost matter a great deal in production. Teams optimize inference with techniques such as quantization, which uses lower-precision numbers, batching multiple requests together, caching, and running smaller distilled models. Inference can run in the cloud on GPUs, or on devices like phones using dedicated neural processing units, which reduces latency and keeps data private.
A specialized processor designed to run AI and machine learning tasks efficiently, often built into phones and laptops.
QuantizationReducing the numerical precision of an AI model's weights, for example from 16-bit to 4-bit numbers, to make it smaller and faster.
Temperature (AI)A setting that controls how random or predictable a language model's output is, with lower values giving more consistent answers.
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