It goes more fundamental than that in the algorithms and it should not take tens of billions of dollars with multiple data centers to train, learn, fine-tune and do inference with these AI models. A decade later, there are no viable alternatives to solve that instead of the costly replacement of hardware with more expensive hardware.
Add that towards scalability and you will realize that training AI models scales terribly with more data as it is very energy and time inefficient. Even if you replace all the hardware in the data centers it still wouldn't reduce the emissions regardless and replacing them also costs at most billions either way. That is my the entire point.
So that does nothing to solve the issue. Only ignores and prolongs it.
> A decade later, there are no viable alternatives to solve that instead of the costly replacement of hardware with more expensive hardware.
I mean, that's the root of scaling as a principle, right?
You could viably start training an AI on your cell phone. It would be completely useless, lack meaningful parameter saturation and take months to reach an inferencing checkpoint, but you could do it. Nvidia is offering a similar system to people, but at a scale that doesn't suck like a cellphone does. Businesses can then choose how much power they need on-site, or rent it from a cloud provider.
If a product like this convinces some customers to ditch older and less efficient training silicon, I don't see how it's any more antagonistic than other CPU designers with perennial product updates.
Which can also be archived by training more with the same amount of spent energy.
Why learn about training ("make training more efficient") on old hardware, which is more energy inefficient?