The need to adapt physical infrastructure design for data centres in the era of AI disruption is important . Can you elaborate on the specific challenges that data centres face when accommodating AI-driven workloads ?

Even though AI is a hot topic , very few people are talking about the physical infrastructure aspect of it . AI presents a different type of workload and technology compared to the traditional , more common x86 two-socket server . New AI workloads have GPU accelerators that run in parallel , operating like one giant computer , which is quite different from the x86 servers , which process workloads and then return to idle mode . They are capable of processing and training data at very high speeds and capacities .

Schneider Electric ’ s white paper outlines key considerations related to power , cooling , racks and software tools in the context of AI . Can you provide insight into some of the most critical considerations and their impact on data centre design ?

In an era of AI disruption , it can be a challenge for organisations to ensure responsible and efficient data centre operations . Steven Carlini , Vice President of Innovation and Data Center , Schneider Electric , tells us how the company ’ s industry-first blueprint for optimising data centres can help find solutions to this .

The servers are different – they are larger , heavier , deeper and have more connections . Frequently , they are now liquidcooled , or will be in the future . There are tremendous changes to power , cooling and racks , which must be beefier to support the weight . In addition to the servers being larger and heavier , they also use more power .

Another consideration is that the density of these racks could range from 30 to 50 to up to 100kW per rack . This denser

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