[MacsHeadroom]

Read The Case for 4 Bit Precision. https://arxiv.org/abs/2212.09720

Spoiler: it's the parameter count. As parameter count goes up, but depth matters less.

It just so happens that at around 10B+ parameters you can quantize down to 4bit with essentially no downsides. Models are that big now. So there's no need to waste RAM by having unnecessary precision for each parameter.

[Taek]

For completeness, there's also another paper that demonstrated you get more power/accuracy per-bit at 4 bits than at any other level of precision (including 2 bits and 3 bits)

[MacsHeadroom]

That's the paper I referenced. But newer research is already challenging it.

'Int-4 llama is not enough [0] - Int-3 and beyond' suggests 3-bit is best for models larger than ~10B parameters when combining binning and GPTQ.

[0] https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-i...