F I N A L W O R D
BUILDING SMARTER: HOW INFRASTRUCTURE IS RESPONDING TO THE DEMANDS OF AI WORKLOADS
Jon Abbott, Technologies Director, Global Strategic Clients at Vertiv discusses AI demand on data centres and how hardware must keep up.
rtificial Intelligence( AI)
A is no longer limited to R & D labs or big tech campuses. From fraud detection in financial services to predictive maintenance in manufacturing, AI has entered production, and it’ s here to stay. But the shift isn’ t just happening in software. Behind the scenes, the critical digital infrastructure needed to support AI is undergoing an evolution too.
Established data centres – which are traditionally designed for predictable, low-variance workloads – are now reaching their limits. AI brings with it dense hardware, erratic power profiles and thermal outputs that strain even the most robust legacy systems. And the challenges are happening now.
For those responsible for IT infrastructure, the pressure is mounting to adapt quickly and intelligently.
What makes AI workloads different?
AI hardware doesn’ t behave like conventional compute. Graphics processing units( GPUs), which form the backbone of most AI systems, are designed for parallel processing. This ability to process vast amounts of information makes them process efficient, but also power-hungry and heat intensive.
Where a typical enterprise server might draw 5 kilowatts to 10 kilowatts, a rack filled with GPU servers can easily demand 30, 50, or even beyond 100 kilowatts. And these loads aren’ t always steady. AI tasks often run in bursts, with workloads ramping from idle to peak output and back in a matter of seconds.
www. intelligentdatacentres. com 59