Training an LLM in Swift, Part 1: Taking matrix mult from Gflop/s to Tflop/s

This is a pretty phenomenal article.

Even for those who don’t care about LLM use, this is just a great article on optimizing Swift performance, which is sadly something that doesn’t have a lot of written material for.

I’m curious if the AMX instructions are truly secret. In theory you could use an M4 or above and get them via SME I think but I’m just guessing as I’ve never tried intrinsic from Swift myself.

Matt Gallagher and CocoaWithLove are major highlights from the early days of my journey in learning iOS development. Awesome to see he is still publishing such high quality information!

> Is 1.1 Tflop/s good? Theoretically, the GPU on my M3 Max is capable of around 15 Tflop/s. But the real ceiling for this kind of task is going to be 3-5 Tflop/s

This is so true. And also why people should not take basic GPU benchmarks so seriously. Getting peak performance out of a GPU is much more complex than it is with a CPU.

And it is one of the reasons why Nvidia still has a software moat compared to other GPU companies. CUDA has so many small kernels tuned for getting peak performance for your dataset.