- cross-posted to:
- technology@lemmy.ml
- cross-posted to:
- technology@lemmy.ml
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.
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There’s such a huge gap between what I read about GPT-5 online, versus the overwhelmingly disappointing results I get from it for both coding and general questions.
I’m beginning to think we’re in the end stages of Dead Internet, where basically nothing you see online has any connection to reality.
Well yeah, it’s a for-profit company. They exist solely to make money, that’s their entire goal.
It’s almost all marketing and has been for a while. ChatGPT peaked with 4o (and 4.5 if you used their API), 4.1 was a step backwards despite them calling it an improvement, and 5 was another step backwards.
They are not any closer to AGI, and we’re not going to see AGI from LLMs no matter how much they claim just how close we are to seeing AGI.
And how many milliwatts does an actual brain use?
It’s shockingly tricky to answer precisely, but the commonly held value is that a human brain runs on about 20w, regardless of the computational load placed on it.
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.
BTW a lot of it seems to be just inefficient coding as Deepseek has shown.
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.
Also don’t forget how people like wasting resources by asking questions like “what’s the weather today”.
And water usage which will also increase as fires increase and people have trouble getting access to clean water
https://techhq.com/news/ai-water-footprint-suggests-that-large-language-models-are-thirsty/
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.
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. There’s no facts here, it’s just “I believe XYX” with nothing to back it up.
We don’t need to make up nonsense about the LLM bubble. There’s plenty of valid enough criticisms as is.
By circulating a dumb figure like this, all you’re doing is granting OpenAI the power to come out and say “actually, it only uses X amount of power. We’re so great!”, where X is a figure that on its own would seem bad, but compared to this inflated figure sounds great. Don’t hand these shitty companies a marketing win.
This figure is already not bad. 40 watt hours = 0.04kWh - you know kWh? That unit on your electric bill that is around $0.18 per kWh (and data centers tend to be in lower cost electric areas, closer to $0.11/kWh.) Still, 40Wh would register on your home electric bill at $0.0072, less than a penny. For comparison, an average suburban 4 ton AC unit draws 4kW - that 40Wh request? 1/100th of an hour of AC for your home, about 36 seconds of air conditioning. I don’t know that this article is making anybody “look bad” in terms of power used.
What exactly do you get for that power though?
The point is that it’s too much power for little gain in return.
Arguably, a great deal more than the energy you lose from opening the door to your house in the summer, once while the A/C is running.
Or, looking at it another way, an AI query+result can be just as valuable as a Tik Tok post / view.
I consider TikTok harmful, so you are right about your last sentence.
But my AC does not nor ever has actually consumed 4kW in an hour.
The average (US suburban 2200sq ft) home’s A/C does consume 4kW while it is cycled on, and in the hotter than normal months of summer it can run continuous duty cycle for hours on end.
You need better insulation then. That’s crazy. Also outdoor blinds.
Me, personally, we have trees and shade. So many subdivisions don’t, and they have dark colored roofs, and then homeowners do bone-headed things like adding “sun rooms” - lots of those in Houston.
We get upset when our electric bill passes $300 for the month, but our neighbors with the 3500 sq ft? They never see it under $400.
For reference, this is roughly equivalent to playing a PS5 game for 4 minutes (based on their estimate) to 10 minutes (their upper bound)
calulation
source https://www.ecoenergygeek.com/ps5-power-consumption/
Typical PS5 usage: 200 W
TV: 27 W - 134 W → call it 60 W
URI’s estimate: 18 Wh / 260 W → 4 minutes
URI’s upper bound: 48 Wh / 260 W →10 minutes
I was just thinking, in more affordable electric regions of the US that’s about $5 worth of electricity, per thousand requests. You’d tip a concierge $5 for most answers you get from Chat GPT (if they could provide them…) and the concierge is likely going to use that $5 to buy a gallon and a half of gasoline, which generates a whole lot more CO2 than the nuclear / hydro / solar mixed electrical generation, in reasonably priced electric regions of the US…
That doesn’t seem right. By my calculations it should be like 5¢. Can you show your work?
Edit: didn’t read. You said “per thousand requests.”
Depends on your electric rates, of course. The gotcha in this statement is “per thousand requests” which cranks up the power usage from 40 watt-hours to 40 kilowatt hours. Say you’ve got “affordable” electricity at 12.5 cents per kilowatt hour: 40 * .125 = 5.
Isn’t this the back plot of the game, Rain World? With the slug cats and the depressed robots stuck on a decaying world when the sapient, organic species all left?
I didn’t know there was such a backstory
that’s a lot. remember to add “-noai” to your google searches.
Or just use any other better search like Bing or duckduckgo. googol sucks and was never any good. Quit pushing ignorant garbage.
Bit of a clickbait. We can’t really say it without more info.
But it’s important to point out that the lab’s test methodology is far from ideal.
The team measured GPT-5’s power consumption by combining two key factors: how long the model took to respond to a given request, and the estimated average power draw of the hardware running it.
What we do know is that the price went down. So this could be a strong indication the model is, in fact, more energy efficient. At least a stronger indicator than response time.
That’s a terrible metric. By this providers that maximize hardware (and energy) use by having a queue of requests would be seen as having more energy use.
And an LLM that you could run local on a flash drive will do most of what it can do.
I mean no not at all, but local LLMs are a less energy reckless way to use AI
