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The interesting point here is that developers targeting the Mac can safely assume that the users will have a processor capable of significant AI/ML workloads. On the Windows (and Linux) side of things, there's no common platform, no assumption that the users will have an NPU or GPU capable of doing what you want. I think that's also why Microsoft was initially going for the ARM laptops, where they'd be sure that the required processing power is available.


> The interesting point here is that developers targeting the Mac can safely assume that the users will have a processor capable of significant AI/ML workloads

Also that a significant proportion (majority?) of them will have just 8 GB of memory which is not exactly sufficient to run any complex AI/ML workloads.


Easy solution; just swap multiple gigabytes of your model to SSD-based ZRAM when you run out of memory. What could possibly go wrong?


I believe MS is trying to standardize this, in the same way as they do with DirectX support levels, but I agree it's probably going to be inherently a bit less consistent than Apple offerings


DirectML can use multiple backends.


That sounds like a big issue, but surely assuming for either case is bad.

I expect OS's will expose an API which, when queried, will indicate the level of AI inference available.

Similar to video decoding/encoding where clients can check if hardware acceleration is available.


How does it help me (with maxed out M3 Max) that Apple might have some chip in the future right now? I do DL on A6000 and 4090, not waiting until Apple produces a chip someday that is faster than 1650 in ML...


That's probably where Microsoft's "Copilot+ PCs" come in.


Plus DirectML, wich as the name implies, builds on top of DirectX, allowing multiple backends, CPU, GPU, NPU.




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