  With the growth of model sizes and scale of their deployment, their sheer
size burdens the infrastructure requiring more network and more storage to
accommodate these. While there is a vast literature about reducing model sizes,
we investigate a more traditional type of compression -- one that compresses
the model to a smaller form and is coupled with a decompression algorithm that
returns it to its original size -- namely lossless compression. Somewhat
surprisingly, we show that such lossless compression can gain significant
network and storage reduction on popular models, at times reducing over $50\%$
of the model size. We investigate the source of model compressibility,
introduce compression variants tailored for models and categorize models to
compressibility groups. We also introduce a tunable lossy compression technique
that can further reduce size even on the less compressible models with little
to no effect on the model accuracy. We estimate that these methods could save
over an ExaByte per month of network traffic downloaded from a large model hub
like HuggingFace.
