From headline-grabbing model training, the focus is steadily shifting to what AI actually does in the real world. Inference, or how trained models make predictions on new data.

AI systems broadly operate in two phases. First is the learning phase, where models are trained on large datasets and their internal parameters are optimized. This stage is computationally heavy and relies on repeated backward passes to update weights. The second is the inference phase, where the already‑trained model runs a single forward pass on new inputs to generate outputs such as predictions, classifications, or responses. Training usually happens offline, while inference happens in real time once the model is deployed.

Chatbots, voice assistants, recommendation engines, medical diagnostic tools, and computer vision systems are all places where AI inference quietly powers the results we see. At scale, serving these inferences can be expensive and energy‑intensive, and today more compute is often consumed by inference than by training. As AI adoption grows, improving inference efficiency alongside model quality and size is becoming a critical design goal.