The University of Rhode Island’s AI lab estimates that GPT-5 averages just over 18 Wh per query, so putting all of ChatGPT’s reported 2.5 billion requests a day through the model could see energy usage as high as 45 GWh.
A daily energy use of 45 GWh is enormous. A typical modern nuclear power plant produces between 1 and 1.6 GW of electricity per reactor per hour, so data centers running OpenAI’s GPT-5 at 18 Wh per query could require the power equivalent of two to three nuclear power reactors, an amount that could be enough to power a small country.
I have an extreme dislike for OpenAI, Altman, and people like him, but the reasoning behind this article is just stuff some guy has pulled from his backside.
A_norny_mousse@feddit.org 5 weeks ago
I don’t care how rough the estimate is, LLMs are using insane amounts of power, and the message I’m getting here is that the newest incarnation uses even more.
BTW a lot of it seems to be just inefficient coding as Deepseek has shown.
ThePowerOfGeek@lemmy.world 5 weeks ago
Kind of? Inefficient coding is definitely a part of it. But a large part is also just the iterative nature of how these algorithms operate. We might be able to improve that via code optimization a little bit. But without radically changing how these engines operates it won’t make a big difference.
The scope of the data being used and trained on is probably a bigger issue. Which is why there’s been a push by some to move from LLMs to SLMs. We don’t need the model to be cluttered with information on geology, ancient history, cooking, software development, sports trivia, etc if it’s only going to be used for looking up stuff on music and musicians.
But either way, there’s a big ‘diminishing returns’ factor to this right now that isn’t being appreciated. Typical human nature: give me that tiny boost in performance regardless of the cost, because I don’t have to deal with. It’s the same short-sighted shit that got us into this looming environmental crisis.
kescusay@lemmy.world 5 weeks ago
Coordinated SLM governors that can redirect queries to the appropriate SLM seems like a good solution.
kautau@lemmy.world 5 weeks ago
And water which will also increase as fires increase and people have trouble getting access to clean water
techhq.com/…/ai-water-footprint-suggests-that-lar…
FauxLiving@lemmy.world 5 weeks ago
It would only take one regulation to fix that:
Datacenters that use liquid cooling must use closed loop systems.
The reason they dont, and why they setup in the desert, is because water is incredibly cheap and energy to cool a closed loop system is expensive. So they use evaporative open loop systems.
rdri@lemmy.world 5 weeks ago
Also don’t forget how people like wasting resources by asking questions like “what’s the weather today”.
ThePinkUnicorn@lemdro.id 5 weeks ago
For training yes, but during operation by this studies measure Deepseek actually has a higher power draw, according to the article. Even models with more efficient programming use insane amounts of electricity
A_norny_mousse@feddit.org 5 weeks ago
OK I guess I didn’t read far enough but your quote says that Deepseek uses less than Open AI?
joonazan@discuss.tchncs.de 5 weeks ago
My guess would be that using a desktop computer to make the queries and read the results consumes more power than the LLM, at least in the case of quickly answering models.
The expensive part is training a model but usage is most likely not sold at a loss, so it can’t use an unreasonable amount of energy.
Instead of this ridiculous energy argument, we should focus on the fact that AI (and other products that money is thrown at) aren’t actually that useful but companies control the narrative. AI is particularly successful here with every CEO wanting in on it and people afraid it is so good it will end the world.