[fouronnes3]

Is a paper that publishes a 0.01% improvement of something at the cost of 5 times more power really an improvement? I believe that every single computer science measurement metric should have Joules or Watts in the denominator. If you are training a model I want to see performance per total energy consumed. If you are measuring inference accuracy, measure PER WATT.

I've always been a bit confused by the apparent tendency of the computer science field to mostly ignore energy and power. We are too often satisfied with the idea that software and programs exist in a perfect whiteboard world of xkcd 505 abstract compute.

[tancop]

open source ai labs care a lot about inference speed. that translates to energy and e waste (gpus that work for less time take longer to wear out). training power is another thing and thats where we see a lot of duplicate work we could fix by making it mandatory to release weights for all models above some total power limit.

if you want to look at the real waste of power just open up some electron app. no good reason why we still use it for new apps in 2026 when gpui and avalonia and tauri are all options

[Imustaskforhelp]

GPUI is still very hard to build things upon the last time (from my limited experimentation) I checked but I wish the team @Zed luck for the GPUI project as I am definitely fascinated by it and its certainly an interesting project for sure!