On a related note it doesn't seem like many local runners are leveraging techniques like PagedAttention yet (see https://vllm.ai/) which is inspired by operating system memory paging to reduce memory requirements for LLMs.
It's not quite what you mentioned, but it might have a similar effect! Would love to know if you've seen other methods that might help reduce memory requirements.. it's one of the largest resource bottlenecks to running LLMs right now!
The hint for me is that the models compress so well, that suggests the information content is much lower than the size of the uncompressed model indicates which is a good reason to investigate which parts of the model are so compressible and why. I haven't looked at the raw data of these models but maybe I'll give it a shot. Sometimes you can learn a lot about the structure (built in or emergent) of data just by staring at the dumps.