If you could train models faster, you’d be able to build larger, more powerful models that outperform the competition.
The fact that Llama 3 is significantly over trained than what was considered ideal even three years ago shows there's a strong appetite for efficient training. The lack of progress isn’t due to a lack of effort. No one has managed to do this yet because no one has figured out how.
I built 1-trit quantized models as a side project nearly a decade ago. Back then, no one cared because models weren’t yet using all available memory, and on devices where memory was fully utilized, compute power was the limiting factor. I spend much longer trying to figure out how to get 1-trit training to work and I never could. Of all the papers and people in the field I've talked to, no one else has either.
Before that, in 2012, Alexnet had to be partially split into two submodels, running on two GPUs (using a form of interlayer grouped convolutions) because it could not fit in 3GB of a single 580 card.
Ternary networks appeared in 2016. Unless you mean you actually tried to train in ternary precision - clearly not possible with any gradient based optimization methods.
What did you try? What were the research directions at the time?
It's missing the colourful commentary that I'd usually give, but alas, we can't have it all.