Isn't that over-simplifying it a bit too much?
replyYou can go another step - a FFN can be simulated on a Turing machine, thus it just exemplifies the incredible semantical power of the Turing machine model of computation. (in fact you don't even need a Turing machine, since there is no looping in one forward pass).
In theory you can run a huge FFN on the tiniest Turing machine, in practice it's much better to run a Transformer on the latest NVIDIA hardware. Or as they say "quantity (performance) has a quality all its own"
I was about to post your last point / quote. Going multigpu is relatively not so though but once you go multi-node you have distributed storage/io/compute system which is highly non trivial. Add that the long training times now you have robustness/fault-tolerantness concerns with hardware failures and restarts. Today’s training systems are engineering marvels.
replyGood point!
replyThere is also the case for Markov chains being theoretically able to do these if tuned well. Or even SAT problem.
"LLM is just fancy autocomplete"
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