this post was submitted on 21 Sep 2026
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Given its extremely high energy and water consumption, the increased minimum system requirements driving premature hardware obsolescence, among other very problematic social and political issues, genAI/LLMs are not compatible with the values and goals of KDE Eco.

KDE is a diverse community and the opinions and projects of some KDE contributors are not representative of the entire community or KDE Eco projects.

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[–] mmmm@sopuli.xyz 21 points 23 hours ago (1 children)

This sounds like KDE Eco is a thing apart from KDE and not a core initiative or value for KDE as a whole. Which is kinda sad imho. I'd imagine a project promoting stuff like eco-friendly FOSS won't let fly shit like the "ai-native desktop" bs, but here we are.

[–] ulterno@programming.dev 0 points 5 hours ago* (last edited 5 hours ago)

That's just a side effect of it being open. Much more so as compared to Gnome.
So, while I may have my reservations regarding AI, I'll not be trying to push out people making AI related KDE software.


Oh shit. I just read the context that post was made in.
Now I'm gonna have to filter through KDE applications myself, to determine which ones are usable.

[–] illusionist@lemmy.zip -5 points 15 hours ago* (last edited 15 hours ago) (4 children)

To put it into perspective how much energy does a promt consume?

I asked an ai. Are the numbers roughly right?

Full Day of Heavy Agent Work (Multiple autonomous runs, multi-agent builds) is ca. 1,000 to 3,000+ Wh which is like 3 to 10 hours of GTA 5

[–] AwesomeLowlander@sh.itjust.works 2 points 11 hours ago

https://ourworldindata.org/how-much-energy-do-data-centers-and-artificial-intelligence-use

TLDR: Energy consumption isn't really high, especially generic conversational prompts. Advanced prompts are only used by a very niche group. The main impact is largely on the locales immediately surrounding data centers.

[–] lime@feddit.nu 2 points 12 hours ago (1 children)

i mean, you can just do the math.

my 7900XTX consumes 300W at full load. my 5700X3D uses 95. say 500W for the entire system working at maximum. at 24GB of VRAM, i can run 20b parameter models at varying speeds, on average about 5-10 tokens per second. in the most generous case, then, it takes 110ish seconds at 500W to generate 1024 tokens which is the limit i've configured. that's 15 Wh per prompt, or 70 prompts per kWh, which isn't really possible to do on that machine since each prompt takes more than a minute.

when playing a game, the hardware isn't all maxed out, call it 350W average, which means a bit less than three hours of gaming per kWh.

if we scale all this up, bearing in mind that specialised hardware is more efficient, a prompt that results in 10 seconds of work for a 2kW server, that's 5Wh, which means 200 prompts per kWh. but these things are running constantly, and with 10-second responses you can fit 360 replies per hour, for a total of 1.8kWh per hour, ideally.

so no, those numbers make no sense at all.

[–] AwesomeLowlander@sh.itjust.works 2 points 11 hours ago (1 children)

10-second responses you can fit 360 replies per hour, for a total of 1.8kWh per hour, ideally.

I find it kinda hard to believe that a prompt takes up an entire server for a full 10 seconds. That does not seem in any way scalable.

[–] lime@feddit.nu 1 points 11 hours ago

context-switching is expensive, but you are right that 10 seconds may be too long. i just pulled something.

then again they've optimised the size of the machines to the point that a single rack can fit more than 100 blade servers, and a dc can have a thousand racks.

[–] punchmesan@lemmy.dbzer0.com 1 points 13 hours ago

There are some things "AI" isn't fit to answer, like questions with nuance. And a "prompt" in itself isn't a quantifiable unit. I can tell you that my gaming rig with 96 GB of RAM and an RTX 2080 could only run a quantized model -- the model weights will eat up my RAM and keep my system drinking electricity. My CPU could handle it like a champ but let's be real, all of that is to get a crappy-performance model doing work.

I could believe that, like, a full day of heavy agent work consumed that much power running the agentic workload on my device, but there's no way that number is accounting for the power consumed in hosted model inference, let alone training.

I hope you're trolling