[yorwba]

Each MoE layer has its own router, and it activates 8 (out of 256) experts at a time. There's no reason to expect all of them to stay on the same GPU, so you're pretty much guaranteed to have to do all-to-all communication between the GPUs in your cluster after every layer for every token.

[phire]

Interesting.

I had assumed the performance advantage for MoE came from minimising traffic between GPUs. But if it's per layer routing, then it's going to massively increase inter-gpu traffic compared to vertical slicing.

I guess that means the performance advantage actually comes when batching thousands of queries? The MoE routing would mean that on each MoE layer, each GPU shard gets a batch of queries that will all hit roughly the same subset of experts (and read the same weights from memory). The batches then shuffle between each MoE layer to re-optimise.

It's kind of like GPU raytracing where you get large performance gains by running coherency sorting on rays and batching similar rays together.

[yorwba]

The performance advantage comes from doing 1/32 of the floating point operations compared to a dense layer with the same number of parameters.

[iamnotagenius]

The performance comes mostly from a fraction of memory bandwidth needed, as LLM are mostly memory constrained. Compute matters too, but usually far less than memory.