[Maxious]

Yep. These Mixture of Experts models are well suited for paging in only the relevant data for a certain task https://huggingface.co/blog/moe

There's some experiments of just removing or merging experts post training to shrink models even more https://bknyaz.github.io/blog/2026/moe/

[vlovich123]

MoE is not suited for paging because it’s essentially a random expert per token. It only improves throughput because you reduce the memory bandwidth requirements for generating a token since 1/n of the weights are accessed per token (but a different 1/n on each loop).

Now shrinking them sure, but I’ve seen nothing that indicates you can just page weights in and out without cratering your performance like you would with a non MoE model

[FuckButtons]

Not entirely true, it’s random access within the relevant subset of experts and since concepts are clustered you actually have a much higher probability of repeatedly accessing the same subset of experts more frequently.

[bee_rider]

That blog post was super interesting. It is neat that he can select experts and control the routing in the model—not having played with the models in detail, tended to assume the “mixing” in mixture of experts was more like a blender, haha. The models are still quite lumpy I guess!