Lots of rumors, but tbh I think it’s highly unlikely they’re using an MoE. MoEs work on batch size = 1 (you can take advantage of sparsity) but not on larger batch sizes. You would need so much RAM and would miss out on the point of using an MoE.
This might be pedantic, but this is a field with so much random vocabulary and it’s better for folks to not be confused.
MoE is slightly different. An MoE is a single LLM with gated layers that “select” which layers to route embeddings/tokens to. It’s pretty difficult to scale and serve in practice.
I think what you’re referring to is more like a model router. You can use a general LLM to “classify” a prompt and then route the entire prompt to a downstream LLM. It’s unclear if this would be faster than a 70B LLM since you would repeat the encoding phase and have some generation, but it could certainly be better.
Hmm, not sure if I track what an encoding layer is? The encoding phase involves filling the KV cache across the depth of the model. I don’t think there’s an activation you could just pass across without model surgery + additional fine tuning.