ChatGPT 5 power consumption could be as much as eight times higher than GPT 4 — research institute estimates medium-sized GPT-5 response can consume up to 40 watt-hours of electricity

Submitted ⁨⁨2⁩ ⁨hours⁩ ago⁩ by ⁨homesweethomeMrL@lemmy.world⁩ to ⁨technology@lemmy.world⁩

https://www.tomshardware.com/tech-industry/artificial-intelligence/chatgpt-5-power-consumption-could-be-as-much-as-eight-times-higher-than-gpt-4-research-institute-estimates-medium-sized-gpt-5-response-can-consume-up-to-40-watt-hours-of-electricity

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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Steve@startrek.website ⁨10⁩ ⁨minutes⁩ ago
And it sucks even worse.

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yesman@lemmy.world ⁨41⁩ ⁨minutes⁩ ago
I think AI power usage has an upside. No amount of hype can pay the light bill.

AI is either going to be the most valuable tech in history, or it’s going to be a giant pile of ash that used to be VC capital.

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kescusay@lemmy.world ⁨23⁩ ⁨minutes⁩ ago
How the hell are they going to sustain the expense to power that? Setting aside the environmental catastrophe that this kind of “AI” entails, they’re just not very profitable.

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- gdog05@lemmy.world ⁨6⁩ ⁨minutes⁩ ago
  Look at all the layoffs they’ve been able to implement with the mere threat that AI has taken their jobs. It’s very profitable, just not in a sustainable way. But sustainability isn’t the goal. Feudal state mindset in the populace is.
  
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A_norny_mousse@feddit.org ⁨1⁩ ⁨hour⁩ 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.

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- ThePowerOfGeek@lemmy.world ⁨1⁩ ⁨hour⁩ ago
  
  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.
  
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  - kescusay@lemmy.world ⁨22⁩ ⁨minutes⁩ ago
    Coordinated SLM governors that can redirect queries to the appropriate SLM seems like a good solution.
    
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eager_eagle@lemmy.world ⁨2⁩ ⁨hours⁩ ago
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.

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nightwatch_admin@feddit.nl ⁨2⁩ ⁨hours⁩ ago
Of course there are comments doubting the accuracy, which by itself is valid, but they are merely doing it to defend AI. IMHO, even at a fifth of the estimates, we’re talking humongous amounts of power, all for a so-so search engine, half arsed chatbots and dubious nsfw images mostly. And let’s not forget: it may be inaccurate and estimates are TOO LOW. Now wouldn’t that be fun?

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- simple@piefed.social ⁨41⁩ ⁨minutes⁩ ago
  
  but they are merely doing it to defend AI.
  
  No they're not, you can agree the research is garbage without defending AI. It literally assumes everything. GPT5 could be using eight times the power. It could be using half the power. It could be using a quadrillion times the power. Nobody knows, because they keep it secret.
  
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Boxscape@lemmy.sdf.org ⁨1⁩ ⁨hour⁩ ago
Image

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- ThePantser@sh.itjust.works ⁨15⁩ ⁨minutes⁩ ago
  OpenAI just needs to harness lightning. Incoming weather control tech.
  
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vegeta@lemmy.world ⁨1⁩ ⁨hour⁩ ago
Image

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DrFistington@lemmy.world ⁨1⁩ ⁨hour⁩ ago
But we get a huge increase in accuracy, from 30% to 30.5%! And it only took 5x the energy consumption!

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brucethemoose@lemmy.world ⁨1⁩ ⁨hour⁩ ago
I don’t buy the research paper at all. Of course we have no idea what OpenAI does, but Deepseek’s publish papers suggest it’s much more than 1 model per node… I think they recommended like a 576 GPU cluster total.

That, and going by the really small active parameter count of gpt-oss, I bet the model is sparse as heck.

There’s no way the effective batch size is 8, it has to be waaay higher than that.

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Valmond@lemmy.world ⁨1⁩ ⁨hour⁩ ago
40Wh or 18Wh which is it?

That’s my old gaming PC running a game for 2min42sec-6minutes … Roughly.

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- TropicalDingdong@lemmy.world ⁨1⁩ ⁨hour⁩ ago
  they vibe calculated it.
  
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