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Not only that, but the switch has the exact same throughput as the actual memory of the V100 until you get to fairly large block sizes. It was shown in a presentation.

For the 300k you'll save just hire a full time dev to write you a distributed implementation of your solver.

Hahha, good one. Just that by the time that one dev implements everything, you'll be overtaken by the competition -- at least that's what everyone's fear is, and it's partly warranted.

Many problems are bandwidth-limited. More cores may indeed actually hurt performance as contention for the limiting resource makes it increasingly hard for any core to run efficiently.

https://superuser.com/questions/2489/cpu-cores-the-more-the-...

You can't spend your way out of the latency issues associated with such a distributed architecture, though.

That's one of things NVLink helps with, actually.

100 GPU rig of 1080ti’s would have 1.1TB VRAM overall though. And most jobs don’t scale well beyond 4 GPUs anyway.

That is problem DGX-2 interconnect solves.

Not for currently available deep learning frameworks.

You're right but only because DGX-2 is not shipping yet. ;P