alt Hacker NewsInference is just tokens transformed through a fixed crystalline structure, no? You already could put that on a chip. There's no particular reason it couldn't be represented as some set of symbolic equations instead of a layered process... it's just another kind of quantization.
When symbolic algorithms are that large, they're equally hard to reason with most of the time. The upshot would be a lot more storage required in exchange for more generalized computing, lessening the need for so much GPU in a lot of cases.
I don't see why a model couldn't be represented that way. After all, if you just polled the output of a model, you could evolve genetic algorithms to predict it with fairly high accuracy in a limited domain. Take that out to the Nth degree and you're basically just unspooling the model into a giant set of equations.
Yes, you certainly could put that on a chip. And people are doing it, for smaller models. The question is how big that chip would be for something like Fable, which is generally estimated in the trillions+ of parameters, and if, given the amount of memory, computation, and bandwidth required - at least with current methods, i.e., very high dimensional matrix math — a chip is the right way to go. Can you put a LM (just a smaller language model) on a chip? Empirically, yes, I believe it is done. Small enough ones probably even fit on a FPGA. Can you put a LLM on a chip? Depends on how L it is! My intuition says that some open smaller models might fit, like Haiku, but not Fable. But that’s just intuition talking.
The hope would be that this unlocks some substantially more efficient or parsimonious math that would fit better on a chip. And that’s clearly my words, not the authors’, per the comment above.