zlacker

>>Dyslex+(OP)
It's unfortunate that no math is ever done in these stories.

If you take the "350,000" H100s that Facebook wants by EOY, each of those can do 700W, which gives you almost 250 MW for just the GPUs. That sounds like a lot, until you realize that a single large power plant is measured in Gigawatts. All of Google's data centers combined are O(10 GW) which are matched with renewable power offsets [1].

Importantly, the world installed >500 Gigawatts of renewable energy in 2023 [2], mostly driven by PV Solar in China. The amount of potential solar and wind and other renewable-ish (hydro) outstrips even a 10x'ing of a lot of these numbers. But even for a single site, dams like Three Gorges are >20 GW.

There are real efficiency and scale challenges in doing AI in a single, large site. But existing power generation systems deliver plenty of power.

[1] https://www.gstatic.com/gumdrop/sustainability/google-2023-e...

[2] https://www.iea.org/reports/renewables-2023/executive-summar...

>>boulos+nm
The big point of course is that there is massive asymmetry between training and inference and that even inference at scale is going to require massive amounts of energy and that likely OpenAI's business model isn't viable at scale. It works right now because they have capital to burn but when the music stops it may well turn out that their model isn't sustainable at all.

Efficiency gains should come first, long before they start looking at alternative energy sources.