I was surprised at previous comparison on omarchy website, because apple m* work really well for data science work that don't require GPU.

It may be explained by integer vs float performance, though I am too lazy to investigate. A weak data point, using a matrix product of N=6000 matrix by itself on numpy:

  - SER 8 8745, linux: 280 ms -> 1.53 Tflops (single prec)
  - my m2 macbook air: it is ~180ms ms -> ~2.4 Tflops (single prec)

This is 2 mins of benchmarking on the computers I have. It is not apple to orange comparison (e.g. I use the numpy default blas on each platform), but not completely irrelevant to what people will do w/o much effort. And floating point is what matters for LLM, not integer computation (which is what the ruby test suite is most likely bottlenecked by)