I believe the applications are mostly what you describe. You take a very general algorithm like a NN or raytracing and scale the model and/or data. For NN, it’

I believe the applications are mostly what you describe. You take a very general algorithm like a NN or raytracing and scale the model and/or data. For NN, it’s quite easy to use a huge amount of memory by making back-propagation chains huge, which is natural for deep models or a recurrent architecture. The model doesn’t have to be huge in a conventional sense; it just has to maintain state e.g., the recurrence. So, a big image model on video classification/segmentation (e.g., self-driving cars) is probably the ideal combination of memory consumption.