While many people are aware that companies use massive datasets to train AI models, they don’t always consider the infrastructure implications of this fact. It’s true that enterprises need GPUs to process all that training data, but there’s much more to AI infrastructure than just GPUs. For one thing, legacy data centers typically lack the power, cooling and connectivity capabilities needed to make the most of GPUs. To overcome these shortcomings, enterprises can deploy GPUs inside high-performance data centers instead. There’s also much more to AI than just training workloads in a few core locations. Today’s AI workloads are inherently…

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