computing equipment, Google’ s facilities only require an additional 0.1 watts for cooling and other infrastructure, whilst typical data centres need 0.67-0.8 watts.
“ The industry is now facing unprecedented demand for new infrastructure solutions to efficiently power, cool and support this next generation of compute and as a result, AI is fundamentally reshaping the architecture of IT infrastructure,” Rajesh Sennik, Head of Data Centre Advisory at KPMG UK, told Data Centre Magazine.
Custom silicon and the move away from commodity hardware The hyperscale operators have fundamentally altered their approach to hardware procurement, moving from off-the-shelf servers to customdesigned systems optimised for their specific workloads.
Amazon’ s Trainium chips are designed specifically for machine learning training workloads, whilst its Inferentia processors handle inference tasks.“ Our new Trainium2 chips offer 30-40 % better price-performance than the current GPU-powered compute instances generally available today,” Amazon CEO Andy Jassy wrote in his 2024 shareholder letter.
Google has taken custom silicon even further with its Tensor Processing Units( TPUs). Its seventh-generation TPU, codenamed‘ Ironwood’, is designed specifically for Google’ s AI workloads and offers performance characteristics that can’ t be achieved with generalpurpose processors.
148 September 2025