In neural network scaling, depth increases performance faster than width, making depth scaling more resource-efficient for smaller models with fewer parameters, while width scaling is more expensive due to higher parameter counts.
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Neural Network Scaling: Depth vs. Width StrategyAdded:
The depth curve kind of goes like this.
It jumps up pretty fast. That's like present throughout our paper. For width, it grows a little bit more slowly. And so that the kind of take away from that is that if you are a bit more resource constrained, scaling along depth might be better cuz there's fewer parameters with a smaller model to a smaller number to a learnable parameters. Width is expensive. Width is expensive, exactly.
And in general, of course, like more parameters is also going to be more expensive. So that's just like another consideration it's to think about when using these networks, I suppose.
>> Yeah.
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