Deep-learning-hype-hater-quick-take: Based on that title (and a glance at the paper), my guess is that rather than brute force and megawatts of GPU power, they found a first-principles solution.
It's very common in mathematics for simpler proofs and extensions of results to follow an initial ground-breaking paper. People looking at this preprint as a refutation of the ML techniques have it exactly backwards.
You have been working on a topic for a while. Then, one day, you learn that someone has managed to get a paper on the topic into Nature/Science/whatever. And not because their results are that impressive but because they are from a famous institute and are using fashionable methods. Of course, the first thing you do is checking if you can improve the results with the tools you already have. If you can, you post the results online and start working on a paper.
That happens all the time.
So AVX ops for the method are not interesting.
"No official code found."
There are known faster methods, that as yet are unproven, that will save giga-watts of power.