I ran into a similar issue years ago, where the base infrastructure occupied the lion's share of the container size, very similar to the sizes shown in the article:

   Ubuntu base      ~29 MB compressed
   PyTorch + CUDA   7 – 13 GB
   NVIDIA NGC       4.5+ GB compressed

The easy solution that worked for us was to bake all of these into a single base container, and force all production containers built within the company to use that base. We then preloaded this base container onto our cloud VM disk images, so that pulling the model container only needed to download comparatively tiny layers for model code/weights/etc. As a benefit, this forced all production containers to be up-to-date, since we regularly updated the base container which caused automatic rebuilding of all derived containers.