[zozbot234]

CUDA is not the real issue, AMD's HIP offers source-level compatibility with CUDA code, and ZLUDA even provides raw binary compatibility. nVidia GPUs really are quite good, and the projected advantages of going multi-vendor just aren't worth the hassle given the amount of architecture-specificity GPUs are going to have.

[bigyabai]

Okay, then don't kill CUDA, just sign CUDA drivers on macOS instead and quit pretending like MPS is a world-class solution. There are trillions on the table, this is not an unsolvable issue.

[atultw]

Admittedly, my use of CUDA and Metal is fairly surface-level. But I have had great success using LLMs to convert whole gaussian splatting CUDA codebases to Metal. It's not ideal for maintainability and not 1:1, but if CUDA was a moat for NVIDIA, I believe LLMs have dealt a blow to it.

[bigyabai]

You can convert CUDA codebases to Vulkan and DirectX code, for all the good it does you. You're still constrained by the architecture of the GPU, and Apple Silicon GPUs pre-M5 are all raster-optimized. The hardware is the moat.

Apple technically hasn't supported the professional GPGPU workflow for over a decade. macOS doesn't support CUDA anymore, Apple abandoned OpenCL on all of their platforms and Metal is a bare-minimum effort equivalent to what Windows, Android and Linux get for free. Dedicated matmul hardware is what Apple should have added to the M1 instead of wasting silicon on sluggish, rinky-dink NPUs. The M5 is a day late and a dollar short.

According to reports, even Apple can't quite justify using Apple Silicon for bulk compute: https://9to5mac.com/2026/03/02/some-apple-ai-servers-are-rep...