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The International Energy Agency states that data centres account for around 1 % of the global electricity demand . By 2030 , data centres are expected to reach 35 gigawatts of power consumption annually , up from 17 gigawatts in 2022 , according to McKinsey .

As explained by Marc Garner , SVP Secure Power Europe at Schneider Electric , AI has emerged as a transformative force , changing the way we process , analyse , and utilise data .

“ With the AI market projected to reach a staggering US $ 407bn by 2027 , this technology continues to revolutionise numerous industries , with an expected annual growth rate of 37.3 % between 2023 and 2030 ,” he tells us .

“ The AI market has the potential to grow even more , thanks to the boom in generative AI ( Gen AI ). 97 % of business owners believe that ChatGPT will benefit their organisations , through uses such as streamlining communications , generating website copy , or translating information , but the surge in adoption will undoubtedly require greater investment and infrastructure for AI-powered solutions than ever .”

Accommodating the demands of this new AI-powered world brings with it challenges .

“ Data centres serve as the critical infrastructure supporting the AI ecosystem ,” Garner says . “ Although AI requires large amounts of power , AI-driven data analytics can help bring data centres closer to net zero and play a positive role in tackling the sustainability challenge .”

Here , Garner explores the four key AI attributes and trends that underpin the physical infrastructure challenges of data

centres : power , racks , cooling , and software management .

How to tackle increasingly power-hungry AI applications As Garner explains , power , cooling , racks and physical infrastructure are core to a data centre ’ s success .

“ Storing and processing data to train machine learning ( ML ) and large language models ( LLMs ) is steadily driving up energy consumption ,” he says . “ For instance , researchers estimate that creating GPT-3

92 March 2024