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.
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.
boguscoder · · focus · HN ↗
mlyle · · focus · HN ↗
Hyperscaler PUE (which includes cooling, power conversion, etc) is typically 1.10-1.15, so multiply 1.5% by 1.1.
The person you replied to already mentioned cooling and multiplied by 1.67 to cover ancillary uses.