Inference

After a model has been trained, it enters the inference stage, where it applies what it has learned to previously unseen data. During inference, the model does not learn or update its parameters. Instead, it processes inputs such as text, images, audio, or numerical data to produce outputs like predictions, classifications, recommendations, or generated content.

Inference is the stage where AI models deliver practical value in real-world applications. The speed, accuracy, and efficiency of inference directly affect user experience, operational costs, and system performance. Optimizing inference is particularly important for large language models and other compute-intensive AI systems.