(Tangent: I wonder why we haven't seen deployment of organic Rankin cycle generators in AI data centers, the exhaust temperature should be compatible and that could yield a 10-20% energy bill saving).
https://eu-mayors.ec.europa.eu/en/news/stockholm-sweden-heat...
I'm talking about making electricity back from the heat (using a low-temp thermodynamic cycle). It has a low yield (due to the low input temperature) but it's usually economically viable when using heat that would end up in the heavens anyway.
Let's say your incoming water temperature is 18C and you want it preheated to 50C, which is 32C degree differential, which means you'll need 32 * 1.5 * 160 = 7680 Wh, or 25 hours straight to heat the buffer tank from scratch.
You'll need to purchase a small water-to-water heat exchanger ($50), two pumps ($100 each), a power supply for said pumps, hose and/or copper pipe and fittings, and various other sundries, plus the cost of a buffer tank ($600ish), so figure all in roughly $1000, plus the cost of electricity to run the pumps.
At $0.22/kWh you're saving roughly $450 a year with this setup in foregone water heating, but because it's not 100% efficient you're spending $500 in electricity to run your GPU 24/7/365, and that's the maximum you can possibly save with the above assumptions. Scale up for more GPUs and down accordingly for less usage as you see fit.
Alternatively, use air as the heat conductor by placing the GPU laden machine in the same space as a hybrid heat pump water heater.
Because there was never a long term plan for AI data centers. It's an AI market capture and cash grab scheme that ends when local models eat their lunch.
Did the rise personal computing make data centers and super computers obsolete?
Any advance in inference that allows local models to do the job will also benefit hyperscalers. Imagine the sheer amount of compute they could throw at problems if each 32GB of VRAM was enough for frontier reasoning.