4 ms·

Is this different from (or related to) the work being done by the exo project? https://github.com/exo-explore/exo https://github.com/exo-explore/exo

• tgtweak 2y ago
  Exo is for partitioning over network across devices (implementing some bandwidth-reducing partitions) but still requires a minimum ram/vram requirement to load a model. This could, in theory, be combined to allow larger models to run on exo clusters with less gpu/ram than is required by the underlying model (at the cost of some performance no doubt, but still).
  • alexandercheema 2y ago
    exo maintainer here. tgtweak is correct. This looks like potentially some promising research that I'm looking into reproducing now. We want to lower the barrier to running large models as much as possible so if this works, it would be a potential addition to the exo offering.
    • tgtweak 2y ago
      Yeah combining these two would make a lot of sense, there is a big appetite to run larger models - even slower - on clustered hardware. This way you can add compute to speed up the token pace vs adding it just to run the model at all. It is also possible some of these optimizations could help optimize distribution based on latency and bandwidth between nodes.