One thing about these numbers that's absolutely shocking to me is how low the energy use is:
> That model’s usage was well within our budget ($68, about 4kWh of energy use / 365 grams of carbon emissions).
The energy cost is literally 1% of the total cost. For context, 4kWh of energy would drive you about 15 miles in an EV, about half of the average person's daily driving miles. It's boiling 10 gallons of water.
With the talk of AI data centers' impact on the world, you'd think this would be 10x to 100x the amount of energy in order to get the effects they're using here.
My takeaway: the AI data center buildout is an overbuild probably at least as large as the fiber buildout that left us with so much dark fiber. If not even bigger. The only thing that will save the economy is the inability of NVIDIA and chip fabs to produce enough chips to match the buildout planned.
> With the talk of AI data centers' impact on the world, you'd think this would be 10x to 100x the amount of energy in order to get the effects they're using here.
This was going so well until this. Everything at scale has environmental impact because you centralize the downside and distribute the upside. This is an important alienation, but it hides the amount of heat, noise and impact on distribution that datacenters have on local infrastructure and environment.
Yes, but, I can run qwen 3.8 flash next (> 100b parameters; ~ opus 4.6) on a 200W tdp desktop.
So, assume 8 of those, and it’s a space heater per house. I have never been disturbed by my neighbor’s space heater.
The problem is centralization, not the absolute energy usage. (And also that LLMs are trending to 100x more efficient than the data center sizing assumed).
Centralization is vastly more energy efficient than decentralization, by orders of magnitude. LLMS love batching, to an insane degree. Running a single conversation through a GPU is about the same cost as doing ~100 in parallel.
Centralization is a huge huge environmental win, far far more than even cloud computing was compared to tons of inefficient, under utilized racks spread though our office buildings.
Totally true. But then it introduces a much greater degree of control, and it creates the aforementioned local concentration of energy usage, noise, etc. So it's a tradeoff, more than a strict win.
epistasis · · focus · HN ↗
> That model’s usage was well within our budget ($68, about 4kWh of energy use / 365 grams of carbon emissions).
The energy cost is literally 1% of the total cost. For context, 4kWh of energy would drive you about 15 miles in an EV, about half of the average person's daily driving miles. It's boiling 10 gallons of water.
With the talk of AI data centers' impact on the world, you'd think this would be 10x to 100x the amount of energy in order to get the effects they're using here.
My takeaway: the AI data center buildout is an overbuild probably at least as large as the fiber buildout that left us with so much dark fiber. If not even bigger. The only thing that will save the economy is the inability of NVIDIA and chip fabs to produce enough chips to match the buildout planned.
gchamonlive · · focus · HN ↗
This was going so well until this. Everything at scale has environmental impact because you centralize the downside and distribute the upside. This is an important alienation, but it hides the amount of heat, noise and impact on distribution that datacenters have on local infrastructure and environment.
hedora · · focus · HN ↗
So, assume 8 of those, and it’s a space heater per house. I have never been disturbed by my neighbor’s space heater.
The problem is centralization, not the absolute energy usage. (And also that LLMs are trending to 100x more efficient than the data center sizing assumed).
epistasis · · focus · HN ↗
Centralization is a huge huge environmental win, far far more than even cloud computing was compared to tons of inefficient, under utilized racks spread though our office buildings.
rpdillon · · focus · HN ↗