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).
replyNow 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
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.
replyIt’s called mixture of experts but it’s not that concepts map cleanly or even roughly to different experts. Otherwise you wouldn’t get a new expert on every token. You have to remember these were designed to improve throughput in cloud deployments where different GPUs load an expert. There you precisely want each expert to handle randomly to improve your GPU utilization rate. I have not heard anyone training local MoE models to aid sharding.
replyis there anywhere good to read/follow to get operational clarity on this stuff?
replymy current system of looking for 1 in 1000 posts on HN or 1 in 100 on r/locallama is tedious.
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!
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