- cross-posted to:
- fuck_ai@lemmy.world
- climate@slrpnk.net
- cross-posted to:
- fuck_ai@lemmy.world
- climate@slrpnk.net
Writing a 100-word email using ChatGPT (GPT-4, latest model) consumes 1 x 500ml bottle of water It uses 140Wh of energy, enough for 7 full charges of an iPhone Pro Max
140Wh seems off.
It’s possible to run an LLM on a moderately-powered gaming PC (even a Steam Deck).
Those consume power in the range of a few hundred watts and they can generate replies in a seconds, or maybe a minute or so. Power use throttles down when not actually working.
That means a home pc could generate dozens of email-sized texts an hour using a few hundred watt-hours.
I think that the article is missing some factor, such as how many parallel users the racks they’re discussing can support.
Datacenter LLM tranches are 7-8 H100s per user at full load which is around 4 kW per second.
Multiply that by generation time and you get your energy used. Say it takes 62 seconds to write an essay (a highly conservative figure).
That’s 68.8 Wh, so you’re right.
Source: I’m an AI enthusiast
Well that’s of the same order of magnitude as the quoted figure. I was suggesting that it sounded vastly larger than it should be.
They’re probably factoring in cooling costs and a bunch of other overhead, I dunno
You are conveniently ignoring model size here…
Which is a primary impact on power consumption.
And any other processing and augmentation being performed. System prompts and other things that are bloating the token size …etc never mind the fact that you’re getting a response almost immediately for something that an at home GPU cluster (not casual PC) would struggle with for many minutes, this isn’t always a linear scale for power consumption.
You are also ignoring the realities of a data center. Where the device power usage isn’t the only power consumption of the location, cooling must be taken into consideration as well. Redundant power switching also comes with a percentage loss in transmission efficiency which adds to power consumption and heat dispersion requirements.
It’s true, I don’t know how large the models are that are being accessed in data centers. Although if the article’s estimate is correct, it’s sad that such excessively-demanding models are always being used for use-cases that could often be handled with much lower power usage.
That’s what I always thought when reading this and other articles about the estimated power consumption of GPT-4. Run a decent 7B LLM on consumer hardware like the steam deck and you got your e-mail in a minute with the fans barely spinning up.
Then I read that GPT-4 is supposedly a 1760B model. (https://en.m.wikipedia.org/wiki/GPT-4#Background) I don’t know how energy usage would scale with model size exactly, but I’d consider it plausible that we are talking orders of magnitude above the typical local LLM.
considering that the email by the local LLM will be good enough 99% of the time, GPT may just be horribly inefficient, in order to score higher in some synthetic benchmarks?
Computational demands scale aggressively with model size.
And if you want a response back in a reasonable amount of time you’re burning a ton of power to do so. These models are not fast at all.
Thanks for confirming my suspicion.
So, the whole debate about “environmental impact of AI” is not about generative AI as such at all. Really comes down to people using disproportionally large models for simple tasks that could be done just as well by smaller ones, run locally. Or worse yet, asking a behemoth model like GPT-4 about something that could and should have been a simple search engine query, which I (subjectively) feel has become a trend in everyday tech usage…