Model Inference
The live phase where a trained AI model processes new, unseen data to make predictions or generate outputs.
Model Inference is the operational phase of the machine learning lifecycle. Once a model has been fully trained, it is deployed into production. When a user or system sends it a prompt or data point, the model calculates and returns a result - this calculation is called inference.
Inference requires massive, real-time computational power. Therefore, optimizing the infrastructure horizon to deliver fast, low-latency inference is a primary challenge in enterprise AI architecture.
Organizations often deploy dedicated on-premises GPU clusters specifically to handle intensive model inference without incurring exorbitant variable public cloud fees.