[apl]

Hard disagree. V100s are a perfectly valid comparison point. They're usually what's available at scale (on AWS, in private clusters, etc.) because nobody's rolled out enough A100s at this point. If you look at any paper from OpenAI et al. (basically: not Google), you'll see performance numbers for large V100 clusters.

[fxtentacle]

Yes and you'll see parameters tuned for V100, not parameters tuned for m1 somehow limping along on a V100 in emulation mode.

I wouldn't complain about a benchmark executing any real world SOTA model on m1 and V100, but those will most likely not even run on the M1 due to memory constraints.

So this article is like using an ios game to evaluate a Mac pro. You can do it, but it's not really useful.

[YetAnotherNick]

You can count the number of GPUs having more than M1 memory(16 GB) in a single hand.

[oblio]

Isn't the M1 GPU memory shared with everything else? Can the GPU realistically used that much? Won't the OS and base apps use up at least 2-3GB?

[qayxc]

The M1 can only address 8 GB with its NPU/GPU.