We don't even have to bother with the detailed math.
For the scale we care about it holds that:
* compute ~= energy
* We can fit more compute into a data centre each year through better tech.
The only reason to build data centres on mass is then to scale compute
massively, which increases energy consumption accordingly.
On 27/07/2026 13:07, Andrei Borzenkov via ghc-devs wrote:
Hi, Tom!
The very core metric of this blogpost is questionable:
> Throughout this post I’ll assume the average ChatGPT query uses 0.3
Wh of energy, about the same as a Google search used in 2009
<https://googleblog.blogspot.com/2009/01/powering-google-search.html>.
Here’s a summary of why 0.3 Wh is the most reasonable guess right now
<https://www.sustainabilitybynumbers.com/p/ai-footprint-august-2025>.
He's listing a bunch of sources in the summary:
> Sam Altman, CEO of OpenAI, published a blog post
<https://blog.samaltman.com/the-gentle-singularity>in June covering a
range of AI updates. In it, he casually mentioned that a standard text
query uses *0.34 Wh of electricity*. Remember, that’s close to the
updated estimate that EpochAI came up with
<https://epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use>(0.3
Wh),
> Mistral AI, another AI company, conducted an environmental analysis
<https://mistral.ai/news/our-contribution-to-a-global-environmental-standard-for-ai>of
its LLMs. ... Overall, the impacts were low: just 1 gram of CO_2 /per
page of text/generated
> This article in MIT Technology Review — We did the math on AI’s
energy footprint. Here’s the story you haven’t heard
<https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/>—
was published in May, and was a good overview of many of the
complexities. Some stand-out numbers: it quoted an estimate of around
*0.93 Wh (let’s call it 1 Wh)*for an average Llama text query response.
And concludes with this:
> The 3 Wh estimate in my previous article was probably too high, and
something a little lower — possibly as low as 0.3 Wh — seems possible.
That looks like a bold hand-waving to my taste. The author dropped MIT
estimation at all and fully agreed with Sam Altman (very questionable
source of information). Mistral AI had relatively small models at a
time (around 70B) and can't be used to estimate GPT.
EpochAI uses a lot of indirect measures for their own research and
base their estimation on a long ago outdated model *GPT-4o* with "200
billion total parameters (likely between 100 and 400 billion)." ©. The
models are way way larger now, even openweight models hit 1 to 3
trillion parameters and have 1 million token context windows.
Both EpochAI and Mistral AI base their estimations on the per-page basis
> I assume that a typical number of output tokens per query is 500
tokens (~400 words, or roughly a full page of typed text). This is
somewhat pessimistic—for example, Chiang et al.
<https://arxiv.org/abs/2403.04132> found an average response length of
261 tokens in a dataset of chatbot conversations
Agentic AIs do not hold this assumption. LLMs now read huge chunks of
files, spawn research subagents, write files with thousands lines of
code and each model supports <think></think> blocks where they echo
and rephrase information multiple times. One agentic session can
easily pass 100k tokens of context and context lookups have quadratic
complexity. Plus closed LLMs from top AI labs hide their thinking
proccess from the end user.
I can't say if "0.3 Wh of electicity" is a wrong estimation for LLM
query, but I don't believe this blogpost estimation because sources of
this metric do not look good.
Cheers,
Andrei.
On 27.07.2026 14:35, Tom Ellis via ghc-devs wrote:
On Sun, Jul 26, 2026 at 11:14:44PM +0200, amindfv--- via ghc-devs wrote:
people who are concerned about, e.g. global warming or loss of drinking water
due to LLMs
I would invite people who are concerned about global warming or water
usage of LLMs to explain what they disagree with in Andy Masley's
analysis. He ultimately concludes that the impact on water and CO2 by
my use of LLMs (all day every working day) is negligible compared to
the impact by other aspects of my daily life:
https://blog.andymasley.com/p/a-cheat-sheet-for-conversations-about?open=false#%C2%A7this-post-in-a-nutshell
Tom
_______________________________________________
ghc-devs mailing list [email protected]
To unsubscribe send an email [email protected]
_______________________________________________
ghc-devs mailing list [email protected]
To unsubscribe send an email [email protected]
_______________________________________________
ghc-devs mailing list -- [email protected]
To unsubscribe send an email to [email protected]
