Kicking learns off on a cluster is surely a thing as well. And in some fields, as you correctly mentioned, memory requirements may be measured in terabytes. It'

Kicking learns off on a cluster is surely a thing as well. And in some fields, as you correctly mentioned, memory requirements may be measured in terabytes. It's more of a 'production use case' though - what I meant is the 'dev use case'. For instance, playing with mid/high frequency market data, plotting various things, just quickly looking at the data and various metrics, trying and testing things out often requires up too 100gb of memory at disposal at any time. It's definitely not only about model training. And 'something smaller to work on' principle doesn't always work in these cases. If the whole thing fits in my local ram, I would of course prefer to work on it locally until I actually need cluster resources. (But seriously though... what is 16gb these days? Looking at process monitor, I think my firefox takes over 5gb now since I have over a thousand tabs in treestyletab, clion + pycharm take another 5gb, parallels vm for some work stuff is another 10gb; if doing any local data science work that's usually at least a dozen gb or a few dozen or more)