Thank you very much @ptrblck  for your reply.
First, there are plenty of these cuboids  ( 4 datasets such as UCSD, AVenue,…). Even considering the possible augmentations, it gets even higher.
yes in fact i read, process the datasets first, to make cuboid and it makes me run out of memory even before  training! (whole 32GB RAM)
some times i think about loading dataset while training ( not pre-loading the all dataset at once) but i think it makes the training so slow ( because GPU should wait for new data samples from CPU, if i am not wrong!).
what is the standard approach for large datasets?