[mikhailt]

While I agree in general, I do want to point out this is a still a lightweight entry level laptop SoC compared to a desktop GPU you've mentioned. It can also run in fanless mode like in MBA m1.

A more modern comparison with mobility GPU would be GTX 1050 Ti mobility, which is around 10-20% faster than 760: https://gpu.userbenchmark.com/Compare/Nvidia-GTX-760-vs-Nvid...

Even still, 1050 ti uses up around 75w of power (2016) and 760 has a TDP of 170W (2013) while M1 GPU is much less than 10W (at full load, it peaks at 16w in Mac mini for the entire SoC, not just GPU).

It would be interesting to see what Apple does when it scales it up to 75w or more for their own custom desktop GPUs which is rumored in development. However, separate desktop GPU does lose the benefits of UMA that makes M1 fast.

[fomine3]

But they created their GPU on world-leading TSMC 5nm. I wonder that M1's GPU perf/watt is mostly came from process advantage. I'd like to see comparison with Kirin 9000. (maybe published by AnandTech)

[sdenton4]

I mean, my entry deep learning machine was a refurbished Linux box with a pile of gpus I found in the electronics recycling bin at work. Later upgraded to a 1080 and then a 2080, as one does.

The nice thing about the desktop is that it can just train for days and I don't need to work about using it for other things, losing time moving locations, etc. It's also still probably cheaper than the m1 laptop, even with a nicer gpu than what you can find in the trash.

[coredog64]

I did the same (well, used a discarded GPU that my kids didn't need any more) and I have one caution: If you're using PyTorch, you'll want to have a CPU that at least supports AVX. The C++ libraries that ship with PyTorch assume AVX at compile time and don't have an option to disable at runtime. It's a PITA to recompile the entire stack.

[p1esk]

Not sure what you are talking about. Pytorch binaries don’t assume anything about your CPU.