Working At Full Power: Data Centers In The Era Of AI

Source: Juan Font | · FORBES · | October 30, 2023

Source: Getty

If ever there was a moment in which technology defined the cultural zeitgeist, it’s now. Artificial intelligence (AI) innovation has shaken every industry and is altering the way we work and live in fundamental, profound, and likely irreversible ways. Notably, Bill Gates has asserted that AI is the most important and revolutionary innovation in 30 years, comparing today’s AI tech race to the emergence of graphical user interfaces in the early 1980s, mobile phones, and the internet itself. It’s exciting, disruptive, and a little bit scary.

Of course, along with the increased adoption of AI tools, new challenges have emerged—particularly in the ways we store, transmit and process data, and our capability of doing so.

Data Centers Working Overtime

Unsurprisingly, AI applications are very power-intensive. Particularly, deep learning models lead to higher processing requirements for data centers because training and executing AI models relies on substantial computational power. Running these applications demands advanced hardware such as GPUs (specialized electronic circuits that accelerate graphics and image rendering) and TPUs (circuits designed to accelerate AI and machine learning workloads).

Traditional data centers are designed with five to 10 kilowatts per rack as an average density; the advent of AI now requires 60 or more kilowatts per rack. Moreover, AI applications generate far more data than other types of workloads and thus require significant amounts of data center capacity.