This is neat. Model weights are split into their layers and distributed across several machines who then report themselves in a big hash table when they are ready to perform inference or fine tuning "as a team" over their subset of the layers.

It's early but I've been working on hosting model weights in a Docker registry for https://github.com/jmorganca/ollama. Mainly for the content addressability (Ollama will verify the correct weights are downloaded every time) and ultimately weights can be fetched by their content instead of by their name or url (which may change!). Perhaps a good next step might be to split the models by layers and store each layer independently for use cases like this (or even just for downloading + running larger models over several "local" machines).