GGML – AI at the Edge

Which low level optimizations specifically are you referring to?

I'm happy with most of the abstractions. We are pushing to assembly codegen. And if you meant things like matrix accelerators, that's my next priority.

We are taking more a of breadth first approach. I think ggml is more depth first and application focused. (and I think Mojo is even more breadth first)

Maybe I'd love to see Tinygrad beat GGML in its own game (4 bit LLM support on M1 Mac GPU or Tensor cores) before adding more backends / models.

It's easy to debug because the generated kernels can be compared to GGML, and still gives something practical that we all can play with.

At this point breadth first is a bit boring, because this way we don't know how far tinygrad is from optimal generated output.

I just deployed tinygrad thanks to this conversation and I've played with just about every local LLM client and toolchain there is. I just ran the examples as listed in the repo with absolutely zero problems and they just worked. I think their goals of prioritizing ease of use far outweighs any performance optimizations at this stage of the game. Nothing is stopping the team from integrating other projects if their performance delta is worth the pivot.

From what I see, the foundation is there for a great multimodal platform. Very excited to see where this goes.