TFRecords serialise the data and convert it to tensors before hand I believe. Plus it uses Protocol Buffers. It doesnt load all records to the memory but loads batched records as far as I know. I had to work on tabular data and I could get about 3X speed increase when using data source as TFRecords instead of CSV using TensorFlow. I am interested in having such faster datasource in Pytorch.
The combination of TFRecords and Dataset API in tensorflow is good. It would be great if we can have the same feature here.