this post was submitted on 23 Nov 2024
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I have read the comments here and all I understand from my small brain is that, because we are using bigger models which are online, for simple tasks, this huge unnecessary power consumption is happening.
So, can the on-device NPUs we are getting on flagship mobile phones solve these problems, as we can do most of those simple tasks offline on-device?
I’ve run an LLM on my desktop GPU and gotten decent results, albeit not nearly as good as what ChatGPT will get you.
Probably used less than 0.1Wh per response.
Is this for inferencing only? Do you include training?
Training is a one time thing. Tge more it get use, the less energy per query it will take
Good point. But considering the frequent retraining, the environmental impacts can only be spread on a finite number of queries.
They have already reached diminishing returns on training. It will become much less frequent soon. Retraining on the same data if there isn't a better method is useless. I think the ressources consumed per query should only include those actually used for inference. The rest can be dismissed as bad faith argumentation.
Inference only. I’m looking into doing some fine tuning. Training from scratch is another story.
Yes, kind of… when those businesses making money out of the subscriptions are willing to ship with the OS for free which something only Apple has the luxury to do instead of OpenAI who doesn’t ship hardware or software (like Windows) beyond an app that’s less than 100MB. Servers would still be needed but not for general cases like help me solve this math or translation. Stable Diffusion or Flux is one example where you only need the connection to internet when downloading a certain model like you wouldn’t necessarily want to download every kind of game in the world when the intention is to play games arises.