I N F O G R A P H I C

Onnec ’ s recent report highlights the importance of holistic design and the challenges that AI is set to bring , inevitably changing the rules for data centres .

With 35 % of global organisations adopting AI , and worldwide AI spend set to reach US $ 300 billion by 2026 , we have now reached a tipping point where AI adoption is becoming exponential . But to realise AI ambitions , data centres will be under huge pressure to meet skyrocketing demand – operators will want to ensure Business Continuity , but also gain and retain customers . AI raises major implications over how to design the data centre of the future . In the rush to meet demand , its important operators tread carefully to avoid making an expensive mistake .

Irreversible implications

AI has taken centre stage and is rewriting everything we know about data centre design . To cater for rising AI demand , operators have been completely rethinking their design approaches . In fact , some hyperscalers hit pause on projects until they better understood the requirements for AI workloads .

The key area under consideration comes down to AI-compute . Data centres have traditionally relied on Central Processing Units ( CPU ) powered racks . But to cater for AI , Graphics Processing Units ( GPU ) will be required , which consume more power , generate additional heat and occupy more space .

This makes decisions regarding the CPU versus GPU allocation during the initial design stage crucial , as essential infrastructure , such as power , cooling or cabling can be difficult to replace once built . To effectively cater to demand , operators need to get the CPU / GPU split right . But to do that , they need a clearer picture of demand to establish whether existing designs , infrastructure and cabling can cope .

Re-designing for AI

Understanding this split will give operators a much clearer view of design requirements and the infrastructure required . AI will dramatically change design requirements in multiple areas , such as :

Power – AI-compute requires highperformance processors ( GPUs and DPUs ) that draw more power than traditional CPUs .

Cooling – With compute demands rising , racks will become more demanding . Liquid cooling is preferred for highperformance chips and can be more cost-effective , but air cooling will still have a role to play .

Networks – The popularity of AI will bring an explosion in network traffic between applications within data centres , between data centres and to end users . Network infrastructure will be under increased pressure , with much higher data throughput than ever before .

Cabling – Poor quality cabling will struggle in intense environments with lots of throughput . Cabling is the foundation for data centre connectivity and a critical component for AI-compute .

Cybersecurity – As well as physical infrastructure , data centre operators also need to consider the shift to AI-assisted data centre operations . Designers must factor in countermeasures to ensure malicious forces cannot sabotage data centre operations .

To future-proof and cope with explosive demand , operators need to take a holistic design approach that considers all aspects of the data centre – from cabling to cooling . When developing future data centres , operators must be mindful that a small change in one area can create ripples that sabotage the performance of an entire data centre . For example , poor cabling can squander the value of highperformance data centre hardware . �

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