How do you conceptualize the usage of CPUS vs GPUS when it comes to AI/ML?

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CPUs can do practically anything but only a relatively few things at a time. GPUs are only good for very specific tasks, but can do hundreds or thousands of those things at a time. GPU is a kind of “math accelerator” for the computer.

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GPUs are expensive parallelization components with non-negligible overhead. Need lots of data and/or very large models to justify their use. Hence their frequent use in DL and virtually nil use with other models, where CPU vectorization (which is crazy fast) does enough

Garden hose vs sprinkler: lots of throughput at one point vs less throughput but at many smaller points

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