> It’s architecturally not a good approach.

Yes, with current LLMs and current hardware and current supporting software this is a true statement. My point wasn't that this approach suddenly changes that, it was that it makes it easier to explore alternatives that might change that. Let's imagine some possibilities:

- Models that use a lot of weight reuse: If you strategically reuse layers 3-4x that could give a lot of time for async loading of future weights.

- Models that select experts for several layers at a time: Same thing, while crunching on the current layer you have teed-up future layers that can be transferring in

- HW makers start improving memory bandwidth: This is already happening right? AMD and Apple are pushing unified memory architectures with much higher bandwidth but still not quite there compared to GPUs. This could lead to a hybrid approach that makes those machines much more competitive. similarly, HW makers could bring back technologies that died on the vine that could help, things like Intel's optaine come to mind. Start making mass storage as fast as system memory is now and the equation may change.

These are quick dart throws that probably have obvious holes in them but the point is platforms like this help us explore paths that appeared dead-end until that one change makes them viable and then allows them to take over. It may not happen. It may be a dead end. But that logic means we will never go out on a limb and try something new. We need people and tech that challenges assumptions and makes it easy for people to try out ideas to keep the tech ecosystem evolving. This does that. Even if this particular project doesn't succeed it is a great thing to do if for no other reason it likely just spurred a bunch of people to try their own crazy hacks for LLM inference. Maybe it even enabled a use case with GPUs that nobody realized existed and has nothing to do with LLMs.