China will most probably win the AI race in the long run because of one major bottleneck the US has - energy. The electric energy and grid buildout in China has been massive since a long time and there is simply no way for the US to quickly catch up.
No matter how much cash you throw you can't just materialize a 100 nuclear reactors to power the data centers.
I was curious how much energy is actually needed to power these datacenters, so I did a little bit of math.
Looking at Nvidia revenues in the past few years, there's maybe $300 billion worth of GPUs currently deployed in the U.S. The B200 costs ~$40k, so we have 7.5 million B200-equivalents, which draw 1000W. Running these at full capacity requires 66 TWh a year, or ~1.5% of total current U.S. electricity consumption. Maybe a bit more to account for inefficiencies, cooling, and other components, but not more than ~2.5% total I would guess.
So it's not that much in reality, but will definitely grow fast.
It's even less if adjusted for non 100% (more like 0.35% of total). Yet factual impact on the grid and other industries will not be as small as numbers appear. For example most probably there will appear transformer (electric) and witchgear deficites. And this is not the only supply chain bottleneck in this subject
margorczynski · · focus · HN ↗
No matter how much cash you throw you can't just materialize a 100 nuclear reactors to power the data centers.
xynelius · · focus · HN ↗
Looking at Nvidia revenues in the past few years, there's maybe $300 billion worth of GPUs currently deployed in the U.S. The B200 costs ~$40k, so we have 7.5 million B200-equivalents, which draw 1000W. Running these at full capacity requires 66 TWh a year, or ~1.5% of total current U.S. electricity consumption. Maybe a bit more to account for inefficiencies, cooling, and other components, but not more than ~2.5% total I would guess.
So it's not that much in reality, but will definitely grow fast.
podgorniy · · focus · HN ↗