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You got it wrong. Inference can use crap GPU's. Training needs the 100x more expensive big guns. Our training machine is 100x more expensive than our inference machine.

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What I'm saying is those 100x more expensive big guns are just normal GP HPC howitzers. Systems that are exclusively designed for AI and nothing else are more or less all just edge inference TPUs.

How is the result of training stored? How big is that? It seems reasonable to assume we’ll eventually plateau and all we’ll need is relatively infrequent training.

I agree, leave the training to open source federations that roll out like operating systems. Minimal training over time.

Then have inference go down to the next layer to use those models as a P2P decentralized network.

Maybe like open router could tap federation networks.

Not so often. The GPU's are running 100% for 3 weeks for a training run. We do images only, but it's the same process. And then we can use the costly GPU's for inference, local model coding agents. Training is about 4x a year. But it depends what ideas the PM or the costumers have. If they has more, more training tasks. Eg. more viruses to detect.

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