The term "data center" indicates a physical facility that stores servers, networking, and other infrastructure necessary to run IT operations in an organization.
TL;DR
An AI data center is infrastructure optimized for the compute, storage, networking, and power that AI workloads demand, often an existing facility extended with accelerators rather than a purpose-built one.
AI splits into two workloads: training, which teaches a model on historical data in dense liquid-cooled clusters, and inference, which runs the trained model in production inside existing air-cooled facilities.
Most organizations run inference, not training, so being AI-ready means matching accelerators, networking, and cooling to the workload and maximizing tokens per second per watt, not building a new facility.
