Just when you thought Nvidia couldn’t get worse, they praise Trump.

But spending a lot of processing power to gain smaller sizes matters mostly in cases you want to store things long term. You probably wouldn’t want to keep the exact same LLM with the same weightings and stuff around in that case.

Ye but that would limit the use cases to very few. Most of the time you compress data to either transfer it to a different system or to store it for some time, in both cases you wouldn’t want to be limited to the exact same LLM. Which leaves us with almost no use case.

I mean… cool research… kinda… but pretty useless.

Ok so the article is very vague about what’s actually done. But as I understand it the “understood content” is transmitted and the original data reconstructed from that.

If that’s the case I’m highly skeptical about the “losslessness” or that the output is exactly the input.

But there are more things to consider like de-/compression speed and compatibility. I would guess it’s pretty hard to reconstruct data with a different LLM or even a newer version of the same one, so you have to make sure you decompress your data some years later with a compatible LLM.

And when it comes to speed I doubt it’s nearly as fast as using zlib (which is neither the fastest nor the best compressing…).

And all that for a high risk of bricked data.

We should first check if ChatGPT is able to handle Air Force One