EDITOR ’ S QUESTION
One of AI workloads ’ most pressing challenges is the demand for specialized and accelerated computing hardware . Traditional CPUs are increasingly inadequate for AI models ’ massive parallel processing needs , particularly in training deep learning algorithms . Consequently , data centres are increasingly adopting hardware accelerators such as graphics processing units ( GPUs ), tensor processing units ( TPUs ) and other AI-specific chips . These specialized processors are designed to meet the high throughput and low-latency requirements of AI workloads .
However , transitioning to AI-specific hardware involves more than merely replacing CPUs with GPUs . Operators must also consider the heightened power and cooling demands associated with more powerful hardware . AI workloads significantly increase power consumption , prompting data centres to understand how to cool these systems efficiently .
Additionally , operators must ensure that their infrastructure is scalable . AI workloads can grow rapidly , often exponentially , as data accumulates and models are refined . Achieving scalability may involve adopting modular data centre designs or deploying disaggregated infrastructure that allows compute , storage and networking resources to scale independently .
AI compute workloads differ from traditional applications in their requirement for vast amounts of data . ML models necessitate training on extensive datasets , often comprising terabytes or even petabytes of information . This creates a pressing need for more effective data storage solutions in terms of capacity and performance .
Traditional storage architectures , such as spinning disk systems , are unlikely to meet the demands of
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