Discussion

@adlrocha Beyond The Code

Lerc: This is one of the basic avenues for advancement.Compute, bytes of ram used, bytes in model, bytes accessed per iteration, bytes of data used for training.You can trade the balance if you can find another way to do things, extreme quantisation is but one direction to try. KANs were aiming for more compute and fewer parameters. The recent optimisation project have been pushing at these various properties. Sometimes gains in one comes at the cost of another, but that needn't always be the case.

LoganDark: We will not see memory demand decrease because this will simply allow AI companies to run more instances. They still want an infinite amount of memory at the moment, no matter how AI improves.

jurgenburgen: If models become more efficient we will move more of the work to local devices instead of using SaaS models. We’re still in the mainframe era of LLM.

redrove: I disagree. I think a sharp drop in memory requirements of at least an order of magnitude will cause demand to adjust accordingly.

DeathArrow: I don't think we are there yet. Models running in data centers will still be noticeably better as efficiency will allow them to build and run better models.Not many people would like today models comparable to what was SOTA 2 years ago.To run models locally and have results results as good as the models running in data centers we need both efficiency and to hit a wall in AI improvement.None of those two conditions seem to become true for the near future.