In a first, Google has released data on how much energy an AI prompt uses
It’s the most transparent estimate yet from one of the big AI companies, and a long-awaited peek behind the curtain for researchers.
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Cool, now how much power was consumed before even a single prompt was ran in training that model, and how much power is consumed on an ongoing basis adding new data to those AI models even without user prompts. Also how much power was consumed with each query before AI was shoved down our throats, and how many prompts does an average user make per day?
I did some quick math with metas llama model and the training cost was about a flight to Europe worth of energy, not a lot when you take in the amount of people that use it compared to the flight.
Whatever you're imagining as the impact, it's probably a lot less. AI is much closer to video games then things that are actually a problem for the environment like cars, planes, deep sea fishing, mining, etc. The impact is virtually zero if we had a proper grid based on renewable.
If their energy consumption actually was so small, why are they seeking to use nuclear reactors to power data centres now?
Because demand for data centers is rising, with AI as just one of many reasons.
But that's not as flashy as telling people it takes the energy of a small country to make a picture of a cat.
Also interesting that we're ignoring something here -- big tech is chasing cheap sources of clean energy. Don't we want cheap, clean energy?
Didn’t xitter just install a gas powered data center that’s breaking EPA rules for emissions?
Yes, yes it did. And as far as I can tell, it's still belching it out, just so magats can keep getting owned by it. What a world
Sure we do. Do we want the big tech corporations to hold the reins of that though?
If cheap(er/better) energy is invented then that's good, why would tech corpos be able to "hold the reins" of it exclusively?
Well, patents and what have you are a thing. I’m mostly thinking that I wouldn’t want e.g. Facebook to run any nuclear reactors or energy grids. That’s something I prefer the government does.
Nuclear reactors already exist, that's not new tech.
I’m not saying it is. I’m saying that predatory companies shouldn’t run critical infrastructure.
We were talking about inventing, not running though.
Sir we do not make reasonable points in here, you’re supposed to hate AI irrationally and shut up.
AI is the driver of the parabolic spike in global data center buildouts. No other use case comes close in terms of driving new YoY growth in tech infra capex spend.
To be fair, nuclear power is cool as fuck and would reduce the carbon footprint of all sorts of bullshit.
Volume of requests and power consumption requirements unrelated to requests made, at least I have to assume. Certainly doesn't help that google has forced me to make a request to their ai every time I run a standard search.
Seriously. I'd be somewhat less concerned about the impact if it was only voluntarily used. Instead, AI is compulsively shoved in every nook and cranny of digital product simply to justify its own existence.
The power requirement for training is ongoing, since mere days after Sam Altman released a very underehelming GPT-5, he begins hyping up the next one.
I also never saw a calculation that took into amount my VPS costs. The fckers scrape half the internet, warming up every server in the world connected to the internet. How much energy is that?
Because the training has diminishing returns, meaning the small improvements between (for example purposes) GPT 3 and 4 will need exponentially more power to have the same effect on GPT 5. In 2022 and 2023 OpenAI and DeepMind both predicted that reaching human accuracy could never be done, the latter concluding even with infinite power.
So in order to get as close as possible then in the future they will need to get as much power as possible. Academic papers outline it as the one true bottleneck.
And academia will work on that problem. It reminds me of intel processors "projected" to use kilowatts of energy, then smart people made other types of chips and they don't need 2000 watts.
Academia literally got cut by more than a third and Microsoft is planning to revive breeder reactors.
You might think academia will work on the problem but the people running these things absolutely do not.
Found the American.
Did the EU suddenly develop a tech industry overnight or are you unaware where all the major AI companies are located?
That's not small....
100's of Gigawatts is how much energy that is. Fuel is pretty damn energy dense.
A Boeing 777 might burn 45k Kg of fuel, at a density of 47Mj/kg. Which comes out to... 600 Megawatts
Or about 60 houses energy usage for a year in the U.S.
It's an asinine way to measure it to be fair, not only is it incredibly ambiguous, but almost no one has any reference as to how much energy that actually is.
That's not ~600 Megawatts, it's 587 Megawatt-hours.
Or in other terms that are maybe easier to understand: 5875 fully charged 100kWh Tesla batteries.
I'd like to understand what this math was before accepting this as fact.
I usually liken it to video games, ya. Is it worse that nothing? Sure, but that flight or road trip, etc, is a bigger concern. Not to mention even before AI we've had industrial usage of energy and water usage that isn't sustainable... almonds in CA alone are a bigger problem than AI, for instance.
Not that I'm pro-AI cause it's a huge headache from so many other perspectives, but the environmental argument isn't enough. Corpo greed is probably the biggest argument against it, imo.
Please show your math.
One Nvidia H100 DGX AI server consumes 10.2kW at 100% utilization, meaning that one hour’s 42 day's use of one server is equivalent to the electricity consumption of the average USA home in one year. This is just a single 8-GPU server; it excludes the electricity required by the networking and storage hardware elsewhere in the data center, let alone the electricity required to run the facility’s climate control.
xAI alone has deployed hundreds of thousands of H100 or newer GPUs. Let’s SWAG 160K GPUs = ~20K DGX servers = >200MW for compute alone.
H100 is old. State of the art GB200 NVL72 is 120kW per rack.
Musk is targeting not 160K, but literally one million GPUs deployed by the end of this year. He has built multiple new natural gas power plants which he is now operating without any environmental permits or controls, to the detriment of the locals in Memphis.
This is just one company training one typical frontier model. There are many competitors operating at similar scale and sadly the vast majority of their new capacity is running on hydrocarbons because that’s what they can deploy at the scale they need today.
I should have specified it was an earlier llama model. They have scaled up to more then a flight or two. You are mostly right except for how much a house uses. It's about 10,500 kW per year, you're off by a thousand. It uses in an hour about 8 hours of house time, which is still a lot though, specially when you consider musks 1 million gpus.
Their first model took 2 600 000 kwh, a plane takes about 500 000. The actual napkin math was 5 flights. I had done the math like 2 years ago but yeah, I was mistaken and should have at least specified it was for their first model. Their more recent ones have been a lot more energy intensive I think.
Thanks for catching, you are right that the average USA home is 10.5MWh/year instead of kWh. I was mistaken. :)
Regarding the remainder, my point is that the scale of modern frontier model training, and the total net-new electricity demand that AI is creating is not trivial. Worrying about other traditional sources of CO2 emissions like air travel and so forth is reasonable, but I disagree with the conclusion that AI infrastructure is not a major environmental and climate change concern. The latest projects are on the scale of 2-5GW per site, and the vast majority of that new electricity capacity will come from natural gas or other hydrocarbons.
A flight to Europe's worth of energy is a pretty asinine way to measure this. Is it not?
It's also not that small the number, being ~600 Megawatts of energy.
However, training cost is considerably less than prompting cost. Making your argument incredibly biased.
Similarly, the numbers released by Google seem artificially low, perhaps their TPUs are massively more efficient given they are ASICs. But they did not seem to disclose what model they are using for this measurement, It could be their smallest, least capable and most energy efficient model which would be disingenuous.
A Megawatt is a unit of power not energy. It means nothing without including the duration, like Megawatt-hours
