Don’t know. Don’t really care honestly […] offset by the fact that I don’t and never will drive.
That’s some strange logic. Either you do know and you can estimate that the offset will indeed “balance it out” or you don’t then you can’t say one way or the other.
Running a 500W GPU 24/7 for a full year is less than a quarter of the energy consumed by the average automobile in the US (in 2000). I don’t know how many GPUs this person has or how long it took to fine tune the model, but it’s clearly not creating an ecological disaster. Please understand there is a huge difference between the power consumed by companies training cutting-edge models at massive scale/speed, compared to a locally deployed model doing only fine tuning and inferencing.
Right, my point is exactly that though, that OP by having just downloaded it might not realize the training costs. They might be low but on average they are quite high, at least relative to fine-tuning or inference. So my question was precisely to highlight that running locally while not knowing the training cost is naive, ecologically speaking. They did clarify though that they do not care so that’s coherent for them. I’m insisting on that point because maybe others would think “Oh… I can run a model locally, then it’s not <<evil>>” so I’m trying to clarify (and please let me know if I’m wrong) that it is good for privacy but the upfront training cost are not insignificant and might lead some people to prefer NOT relying on very costly to train models and prefer others, or a even a totally different solution.
The model exists already — abstaining from using it doesn’t make the energy consumption go away. I don’t think it’s reasonable to let historic energy costs drive what you do, else you would never touch a computer.
Indeed, the argument is mostly for future usage and future models. The overall point being that assuming training costs are negligible is either naive or showing that one does not care much for the environment.
From a business perspective, if I’m Microsoft or OpenAI, and I see a trend to prioritize models that minimize training costs, or even that users are avoiding costly to train model, I will adapt to it. On the other hand if I see nobody cares for that, or that even building more data center drives the value up, I will build bigger models regardless of usage or energy cost.
The point is that training is expensive and that pointing only to inference is like the Titanic going full speed ahead toward the iceberg saying how small it is. It is not small.
If you’re a company you don’t care what the home user does. They didn’t pay for the model and so their existence in the first place indicates a missed opportunity for market share.
No one is saying training costs are negligible. They’re saying the cost has already been paid and they had no say in influencing it then or in the future. If you don’t pay for it and they can’t tell how often you use it they can’t really be influenced by your behavior.
It’s like being overly concerned with the impact of a microwave you found by the road. The maker doesn’t care about your opinion of it because you don’t give them money. The don’t even know you exist. The only thing you can meaningfully influence is how it’s used today.
I see. Well, I checked your post history because I thought “Heck, they sound smart, maybe I’m the problem.” and my conclusion based on the floral language you often use with others is that you are clearly provoking on purpose.
Unfortunately I don’t have the luxury of time to argue this way so I’ll just block you, this way we won’t have to interact in the future.
That’s some strange logic. Either you do know and you can estimate that the offset will indeed “balance it out” or you don’t then you can’t say one way or the other.
Running a 500W GPU 24/7 for a full year is less than a quarter of the energy consumed by the average automobile in the US (in 2000). I don’t know how many GPUs this person has or how long it took to fine tune the model, but it’s clearly not creating an ecological disaster. Please understand there is a huge difference between the power consumed by companies training cutting-edge models at massive scale/speed, compared to a locally deployed model doing only fine tuning and inferencing.
I specifically asked about the training part, not the fine tuning but thanks to clarifying.
Edit : you might be interested in helping with https://lemmy.world/post/30563785/17397757 please
The point is that OP (most probably) didn’t train it — they downloaded a pre-trained model and only did fine-tuning and inference.
Right, my point is exactly that though, that OP by having just downloaded it might not realize the training costs. They might be low but on average they are quite high, at least relative to fine-tuning or inference. So my question was precisely to highlight that running locally while not knowing the training cost is naive, ecologically speaking. They did clarify though that they do not care so that’s coherent for them. I’m insisting on that point because maybe others would think “Oh… I can run a model locally, then it’s not <<evil>>” so I’m trying to clarify (and please let me know if I’m wrong) that it is good for privacy but the upfront training cost are not insignificant and might lead some people to prefer NOT relying on very costly to train models and prefer others, or a even a totally different solution.
The model exists already — abstaining from using it doesn’t make the energy consumption go away. I don’t think it’s reasonable to let historic energy costs drive what you do, else you would never touch a computer.
Indeed, the argument is mostly for future usage and future models. The overall point being that assuming training costs are negligible is either naive or showing that one does not care much for the environment.
From a business perspective, if I’m Microsoft or OpenAI, and I see a trend to prioritize models that minimize training costs, or even that users are avoiding costly to train model, I will adapt to it. On the other hand if I see nobody cares for that, or that even building more data center drives the value up, I will build bigger models regardless of usage or energy cost.
The point is that training is expensive and that pointing only to inference is like the Titanic going full speed ahead toward the iceberg saying how small it is. It is not small.
If you’re a company you don’t care what the home user does. They didn’t pay for the model and so their existence in the first place indicates a missed opportunity for market share.
No one is saying training costs are negligible. They’re saying the cost has already been paid and they had no say in influencing it then or in the future. If you don’t pay for it and they can’t tell how often you use it they can’t really be influenced by your behavior.
It’s like being overly concerned with the impact of a microwave you found by the road. The maker doesn’t care about your opinion of it because you don’t give them money. The don’t even know you exist. The only thing you can meaningfully influence is how it’s used today.
It’s literally what the person I initially asked said though, they said they don’t know and don’t care.
Herpa Derpa flurbidy
I see. Well, I checked your post history because I thought “Heck, they sound smart, maybe I’m the problem.” and my conclusion based on the floral language you often use with others is that you are clearly provoking on purpose.
Unfortunately I don’t have the luxury of time to argue this way so I’ll just block you, this way we won’t have to interact in the future.
Take care and may we never speak again.
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