[admax88qqq]

> One day, maybe not far from now, a breakthrough will allow huge LLMs (say 200B in size) to run well on an old 5 year old Dell desktop.

But if you have such a breakthrough could you not also apply it and run 200T models on todays datacenters?

[ACCount37]

Not only you could: you would also want to.

The likes of Mythos show that the scaling laws are real, and you can x5/x2 the total/active params and get meaningful gains. If "inference per param" gets cheaper? Up the params and get more intelligence for the same price.

[pennomi]

That assumes scaling laws still hold up. A bigger model might end up only incrementally more intelligent.

[ACCount37]

They do. Mythos kicked ass while it lasted. And what we know of the scaling law curves promises us even more gains in the future.

"The future" being "whenever training and inference at increased scale becomes economical". Which is probably bounded by new generations of hardware, but might also be pushed forward by algorithmic advances.

[phkahler]

I think they're out of training data though...

[ACCount37]

Synthetics are often used for "data amplification" nowadays. Extra compute covers a multitude of sins.

[deweywsu]

Quite true