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.

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.

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.

I hope you're trolling