Ahh got it, thanks for the pointer. I am surprised there is enough correlation there to allow an entire GPU to be specialized. I'll have to dig in to the paper again.
If you look deeper, many of these are only applicable to training (we already do FP8 for inference, MTP is to improve training convergence, and DualPipe is to overlapping communication / compute mostly for training purpose too). The efficiency improvement on inference IMHO is overblown.
we already do FP8 for inference
Let's say you have an inference batch of 128 chats, at layer `i` you take the hidden states, compute their routing, scatter them along with the KV for those layers among GPUs (each one handling different experts), the attention and FF happens on these GPUs (as model params are there) and they get gathered again.
You might be able to avoid the gather by performing the routing on each of the GPUs, but I'm generally guessing here.