Yes, its odd…if just do a normal 224x224 batch loader for resnets and increase batch size, you get the expected behavior of increasing efficiency with larger batch size. But for my particular problem, in which there is a multi-task loss (and thus am calling next(dataloader_iterator_i) multiple times per loop for each dataloader, and many image transformations, I’m seeing a slight slow down as batch size goes to 256 from 32…maybe by about 4%. GPU utilization is still 100% to my knowledge, although I can only tell this by sampling nvidia-smi views, so it could be falling down more periodically. It would be great tho to know how to inspect whats going on under the hood with respect to the parallelism between loading and training.