I could be very wrong on how experts work across layers though, I have only done a naive reading on it so far.
I suppose you could look at what part of each layer was most likely to trigger together and segregate those by GPU though
This doesn't sound like it would work if you're running just one chat, as you need all the experts loaded at once if you want to avoid spending lots of time loading and unloading models. But at scale with batches of requests it should work. There's some discussion of this in 2.1.2 but it's beyond my current ability to comprehend!
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.
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