I believe what OP is talking about, in the context of ML models, is that reading 1 byte of memory on the GPU from the GPU’s memory is _much_ slower than the CPU reading from the system RAM.
This is an intentional choice and it speaks to the core design of what each system is solving for. CPUs trade low latency for faster single threaded execution speed. GPUs trade high latency for “total thread” execution speed.
CPUs solve for maximum “single thread” performance (for lack of a better term). If you have an operation that reads and mutates one byte of RAM, and then stack many of those instructions into a long sequence, the CPU is very fast at executing that. Most programs we write do this. Processing the steps for a single-thread of an application.
GPUs optimize for concurrency and they do that by running many “threads”. Each thread is often memory bound, but because they run in parallel when one “blocks” on reading memory, another thread just pops in it’s place until it has to block.
GPUs are constantly swapping active “threads” running and are able to hide the latency better. And, because of that design, you can trade for higher bandwidth and get more instructions out.
Which, for image data (what GPUs were originally designed for), you’re manipulating big two dimensional arrays of pixel data where concurrency is important. CPUs have instructions like SSE/AVX that sit somewhere in between, these days, but GPUs have the advantage of being able to target only one domain instead of both.
That’s my understanding of it, at least. I was a game dev in a former life :)