The term cloud computing evokes an ethereal impression of blue skies with scattered patches of white clouds. In reality, cloud computing happens in industrial datacenters that are typically housed in brutalist concrete buildings embodying massive material resource consumption (see Figure 8-1).1 Within each center, hundreds of dense racks house thousands of humming CPUs and GPUs that must be cooled either by powerful fans or by liquid cooling systems.
The current class of GenAI models relies on cloud computing to function. The actual scale, capacity, and cost of these datacenters for AI use is not always known. However, some predictions estimate a threefold increase in capacity by 2030, fueled primarily by AI-related demands.2
Consider the case of Meta AI Research’s planned 2GW+ datacenter that, according to CEO Mark Zuckerberg, “would cover a significant part of Manhattan,” bringing more than 1.3 million GPUs online. Powering datacenters at this scale requires vast amounts of electricity, sometimes sourced from dedicated power plants. The International Energy Agency (IEA) estimates that by 2026, datacenters will use approximately 800TWh worldwide, almost 4% of the total global electricity demand.
Figure 8-1. A datacenter as imagined by the text-to-image generative model DALL-E introduced by Aditya Ramesh et al. in “Zero-Shot Text-to-Image Generation” ...
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