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The title is nonsensical. The faster the compute is, or the faster inference is (through eg precision), the larger the models people will train, because accurac
by make3 4y ago
The title is nonsensical. The faster the compute is, or the faster inference is (through eg precision), the larger the models people will train, because accuracy / output quality increases indefinitely with model size, and everyone knows this. So a different precision it will not "Solve the AI/ML Overhead", that's nonsense. People will just use as large a model as they can for their latency budget at inference & for their $ budget at training, whatever it is.
- psychphysic 4y agoA modern day analog to Jevon's Paradox.
- naasking 4y agoChinchilla suggests that most models now are undertrained. It's been the lore that model size was the bottleneck but we've since passed that and now training data is the limiting factor.
- make3 4y agochinchilla only says that it would be more computationally efficient to have more data than to make the model larger, not that making the model larger wouldn't still benefit in performance gain