Are you asking why pay extra for a CPU and RAM? Not everything can be done on a GPU, for example, .png decompression. If you really analyzed your training code and preprocessed your data substantially you could probably get away with very lightweight CPU/RAM resources but I think the reality is that it's such a minor contribution of cost to the overall system (GPU are expensive) that wasting development cycles on that degree of optimization isn't strictly necessary. When you're a hyperscaler you are likely chasing those fractions of a percent of cost efficiency though. To use my original example, you would likely want to preprocess your .png to either .webp (multi-threaded lossless) or .jpeg (lossy), but likely it wouldn't make sense to turn it into a GPU decompressible format as you would save on CPU cost during training but would pay more in storage (and maybe transfer) cost.

Edit: To be more clear, if the CPU work is bottlenecking training, you want to optimize that as much as possible by preprocessing your data/tweaking training scripts. What I'm discussing here is the gap between "fast enough" and "faster":

CPU is not fast enough for training < CPU is exactly fast enough for training < CPU is faster than needed for training