[jeff_friesen]

I somewhat disagree. Maybe there are few compiled applications that can use those resource, but it's easy to write code that uses all of them.

I have a 40 core CPU machine with a GTX 1080 Ti GPU. I run deep learning models with 90% GPU utilization and those 40 cores are barely used.

I would love to have 3 more GPUs to run in parallel to test different neural network architectures. Sometimes I'll run a CPU script at the same that processes machine learning data that uses all 40 cores.

I would use 4000 CPU cores and 10 GPUs in a machine if I could get them and I don't even do machine learning full time. I'm personally happy to see this trend of more core counts.

[mugsie]

This is my point - I dont think it is as easy as you think to scale from 3.5k cores in a 1080ti to 14k ish cores.

Sure you can write code that nominally use all the cores, but I do not think that the performance increase is going to be linair to the core count.

It's not even down to CPU core count - it would be limited by the speed of a single core.

[visarga]

If you run algorithms from sklearn or just plain numpy it would make great use of the CPU. I was surprised to see the CPU just as useful as the GPU, maybe even more, in my ML projects.