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For my workload (optical flow) I was honestly surprised to see that the Google Cloud V100 was not faster than my local GTX 1080. So I guess that varies a lot by how you're training, too.

For many of my AI training workloads, already the 1080 is "fast enough" and the CPU or SSDs are the bottleneck. In that case, GPU doesn't really matter that much.

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Yes that might be the case. In my case I mostly trained big (tens to hundreds of millions of parameters) networks mostly made of 3x3 convolutions, and I think the V100 has dedicated hardware for that. Then as I mentioned you can get a further 2x speedup by using half precision.

If you train smaller models, or RNN, you probably lose most of the gains of dedicated hardware. But I guess that for this same reason the experiments in the article are little more than a provocation, I don't know if you could train a big network in finite time on M1 chips...

That said, of course, if the budget was mine, I wouldn't buy a V100 :-)

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