(no title)
MediaSquirrel | 5 months ago
The only reason to use CoreML these days is to tap into the Neural Engine. When building for CoreML, if one layer of your model isn't compatible with the Neural Engine, it all falls back to the CPU. Ergot, CoreML is the only way to access the ANE, but it's a buggy all-or-nothing gambit.
Have you ever actually shipped a CoreML model or tried to use the ANE?
llm_nerd|5 months ago
This is nonsensical.
MLX and CoreML are orthogonal. MLX is about training models. CoreML is about running models, or ML-related jobs. They solve very different problems, and MLX patches a massive hole that existed in the Apple space.
Anyone saying MLX replaces CoreML, as the submission does, betrays that they are simply clueless.
>The only reason to use CoreML these days is to tap into the Neural Engine.
Every major AI framework on Apple hardware uses CoreML. What are you even talking about? CoreML, by the very purpose of its design, uses any of the available computation subsystems, which on the A19 will be the matmul units on the GPU. Anyone who thinks CoreML exists to use the ANE simply doesn't know what they're talking about. Indeed, the ANE is so limited in scope and purpose that it's remarkably hard to actually get it to use the ANE.
>Have you ever actually shipped a CoreML model or tried to use the ANE?
Literally a significant part of my professional life, which is precisely why this submission triggered every "does this guy know what he's talking about" button.
MediaSquirrel|5 months ago
https://github.com/ml-explore/mlx-swift
Maybe I am working on a different set of problems than you are. But why would you use CoreML if not to access ANE? There are so many other, better newer options like llama.cpp, MLX-Swift, etc.
What are you seeing here that I am missing? What kind of models do you work with?