Local inference. I imagine they have an interest in making this and other cutting edge models small enough to be possible to do quick inference on their desktop machines. The article shows that, with Figure 1E demonstrating inference on an M2 Max 64 GB.

Frankly, it's a great idea. If you are a small pharma company, being able to do quick local inference removes lots of barriers and gatekeeping. You can even afford to do some Bayesian optimization or RL with lab feedback on some generated sequences.

In comparison, running AlphaFold requires significant resources. And IMHO, their usage of multiple alignments is a bit hacky, makes performance worse on proteins without close homologs, and requires tons of preprocessing.

A few years back, ESM from Meta already demonstrated that alignment-free approaches are possible and perform well. AlphaFold has no secret sauce, it's just a seq2seq problem, and many different approaches work well, including attention-free SSMs.

I think people often interpret a bit too much. Perhaps it’s just some researchers who got enough freedom to run and publish interesting work within apple. For a company like apple it makes sense to have a research lab with considerable freedoms even if protein folding is not a core interest, which is why you see it published but not the formula for the new Corning Gorilla glass…

Will be fascinating to see how the market breaks down in the future, will enough people want a third best model they can run on prem, or will people all be fighting in line for the top models that are a few cents more per token on supercomputers.