Bullshit. It has to be way more than that.
As stated above, our study focuses on the inference (i.e. deployment) stage in the model life cycle,
And this is why.
Making an image with generative AI uses as much energy as charging your phone
Submitted 11 months ago by misk@sopuli.xyz to technology@lemmy.world
Comments
Illecors@lemmy.cafe 11 months ago
Mahlzeit@feddit.de 11 months ago
The model cards for Stable Diffusion 1.5 and 2.1 estimate the CO2 emissions as 11.25 tons and 12 tons for training. XL lacks the info.
A transatlantic flight (round-trip) is about 1 ton per pax. So, while every little bit helps, ML is not where you can make the big gains in lowering emissions.
Even_Adder@lemmy.dbzer0.com 11 months ago
If I remember correctly, SDXL is a heavily modified SD2.1, so the numbers might be similar.
greater_potater@lemmy.world 11 months ago
Wait so then does playing a game that maxes out my GPU for two hours use enough power to charge 1000 smartphones?
Because that’s a lot.
DreadPotato@sopuli.xyz 11 months ago
A high(er) end smartphone has a battery capacity of approx. 0.019kWh (5000mAh), a gtx3080 has a max power draw of 320W so running that (at max load) for two hours is 0.64kWh, which is equivalent to fully charging ~34 smartphones.
Cocodapuf@lemmy.world 11 months ago
Thanks for actually doing the math.
kelvie@lemmy.ca 11 months ago
While it is good to be cognizant of this, playing AAA games for the same amount of time as the inference is the same as this, right? Since they use the same GPU on consumer hardware.
bioemerl@kbin.social 11 months ago
This is outdated in a big way with stable diffusion turbo and the recent LCM models that can render images at 30fps on a 3090.
360w * 1s /60 seconds a minute / 60 minutes an hour = .1 wh/image
Or 100 mwh
30 images a second? 3.3 mwh
A phone battery is 3000 mah * 3.5volts 10,500 mwh
3,181 images per phone charge.
My math is probably off, but you get the idea.
Illecors@lemmy.cafe 11 months ago
You’re off by 3 orders of magnitude.
30 * 0.1Wh = 3Whbioemerl@kbin.social 11 months ago
That's 100mwh for a second of 3090 time/ 30 images a second.
If a 3090 was sucking down 3WH for 1/30th a second it would melt.
Cyclist@lemmy.world 11 months ago
Take away all those extra fingers and hands to save energy.
Even_Adder@lemmy.dbzer0.com 11 months ago
DABDA@lemmy.world 11 months ago
FaceDeer@kbin.social 11 months ago
I pay for electricity. When I do an activity that requires electricity the cost of that factors into whether I do that. I don't see the issue here.
guitarsarereal@sh.itjust.works 11 months ago
Tons of work being done to improve the energy efficiency of ML models. We’re in the ENIAC days of AI right now. I’m not sure I see the problem other than that it would obviously be nicer if we could just build a time machine and steal an energy efficient AI from the year 2100.
raptir@lemdro.id 11 months ago
It’s probably a net savings over a digital artist creating images given the speed. Just powering your monitor for so much longer is going to take more power.
RobotToaster@mander.xyz 11 months ago
The referenced part of the paper, for those interested in the maths.
Text-based tasks are, all things considered, more energy-efficient than image-based tasks, with image classification requiring less energy (median of 0.0068 kWh for 1,000 inferences) than image generation (1.35 kWh) and, conversely, text generation (0.042 KwH) requiring more than text classification (0.0023 kWh). For comparison, charging the average smartphone requires 0.012 kWh of energy 4, which means that the most efficient text generation model uses as much energy as 16% of a full smartphone charge for 1,000 inferences, whereas the least efficient image generation model uses as much energy as 950 smartphone charges (11.49 kWh), or nearly 1 charge per image generation, although there is also a large variation between image generation models, depending on the size of image that they generate.
autotldr@lemmings.world [bot] 11 months ago
This is the best summary I could come up with:
Their work, which is yet to be peer reviewed, shows that while training massive AI models is incredibly energy intensive, it’s only one part of the puzzle.
For each of the tasks, such as text generation, Luccioni ran 1,000 prompts, and measured the energy used with a tool she developed called Code Carbon.
Generating 1,000 images with a powerful AI model, such as Stable Diffusion XL, is responsible for roughly as much carbon dioxide as driving the equivalent of 4.1 miles in an average gasoline-powered car.
AI startup Hugging Face has undertaken the tech sector’s first attempt to estimate the broader carbon footprint of a large language model.
The generative-AI boom has led big tech companies to integrate powerful AI models into many different products, from email to word processing.
Luccioni tested different versions of Hugging Face’s multilingual AI model BLOOM to see how many uses would be needed to overtake training costs.
The original article contains 1,021 words, the summary contains 153 words. Saved 85%. I’m a bot and I’m open source!
Zarxrax@lemmy.world 11 months ago
No shit that using your PC for any purpose will consume electricity. A modern GPU can generate an image on a couple of seconds. Or I could just play a video game for an hour, and consume a few thousand times more energy
Even_Adder@lemmy.dbzer0.com 11 months ago
Yeah, I can’t imagine it’s that different from playing a demanding game. I hear my video card fans spin up harder and sustain that speed for the duration of a play session.
Mahlzeit@feddit.de 11 months ago
If you generate images for an hour, it might be about the same as playing a game, depending on how fast you prompt.
But you’re quite right. For most end users it’s entertainment, so this is the proper context.