The study that suggests 10-50 interactions with ChatGPT evaporates a whole bottle of water, doesn’t account for the fact that cooling systems are enclosed…

…and that “study” is based on a bunch of assumptions, which include evaporation from local power plants, as well as the entire buildings GPT’s servers are located in. It does this as if one user is served at a time, and the organizations involved (such as microsoft) do nothing BUT serve one use at a time. So the “study” (which isn’t peer reviewed and never got published) pretends those buildings don’t also serve bing, or windows, or all the other functions microsoft is involved with. It instead assumes whole buildings at microsoft are dedicated to serving just one user of ChatGPT at a time.

It also includes the manufacture of all the serve and graphics cards equipment, even though the former was used before ChatGPT, and will be used for other things as well… and the latter is only used in training.

You can check the study out yourself here:

http://arxiv.org/pdf/2304.03271

It’s completely junk. Worthless. Even uses a click bait title, and keeps talking about “the secret water foot print” as if it’s uncovering some conspiracy. It’s bunk science.

P.S It also doesn’t seem to understand that the bulk of GPT’s training was a one time cost, paid in 2021, with one smaller update in 2023.

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

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.

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?