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"For many years, AMD GPUs were cheaper and equally or more performant than NVIDIA GPUs. Did that stop NVIDIAs lead in AI training? No, software support is the key component. It's the same with Apple's M1. I don't know anyone who is using it. People need obscure x64 instructions and CUDA. And that means you buy AMD CPU + NVIDIA GPU as the cheapest high-performance combination."

Riiight, but, OP isn't necessarily talking purely from an AI/ML use case.

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Well if you don't need the high performance, then I'd expect people will go for a cheap CPU, meaning no M1 Ultra.

ML training isn't the only application of high performance computing. Apple is making video editing machines - which is why they have the mix of cores that they do. Simulations are also high-performance computing applications.

> Apple is making video editing machines - which is why they have the mix of cores that they do.

Nope. They have that mix because they've decided to prioritize power consumption reduction (I suspect because they sell way, way more laptops that desktops/servers). It's actually a PITA for developers (you have to ensure that your compute-heavy threads are always on the performance cores), and conceptually confusing for users if they ever have to confront it. For pure performance, with no concern for power consumption, there are faster solutions.

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