Yep. That is exactly the idea here. Our compression method is super duper naive. We literally keep every n-th weight column and discard the rest. Turns out that even after getting rid of 80% of the weight columns in this way, we were able to retain the same performance in a 125M GPT.

If you have things organized neatly together, you can also use pre-existing compression algorithms, like JPEG, to compress your data. That's what we're doing in Self-Organizing Gaussians [0]. There we take an unorganised (noisy) set of primitives that have 59 attributes and sort them into 59 2D grids which are locally smooth. Then we use off-the-shelf image formats to store the attributes. It's an incredibly effective compression scheme, and quite simple.

[0]: https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/