Comment by pizza234
9 hours ago
> but are they functionally useful or helpful to anyone currently?
Yes and no, depend on your expectations. Some/many like to run local LLMs just for the sake of it, so anything will do.
MoE are useful on PC systems, at the condition of having high enough memory bandwidth (and large amounts of RAM) - that is, Threadripper/Pro.
The advantage of MoE is that only a subset of the model's experts is used for each token, so not all weights need to be present in VRAM at once. The remaining weights can reside in system RAM, although moving and accessing them still carries a substantial performance cost (and that's why high memory bandwidth is needed).
Does MoE help with multimodality? Can it in general enable reasoning in imagery (technical drawings, diagrams, schematics) rather than text-based?
MoE has nothing to do with multimodality.
MoE is a concept proposed in 1991, before the deep learning era (which is before what I call the transformers era). You can think of it like sharing.
Contrary to popular belief; 'experts' in MoE LLMs do not specialize. There's no expert trained to be good at maths, or python, or writing, or whatever. It's an inference optimization.
As for reasoning in non-text modalities, you might find this paper interesting :) https://huggingface.co/papers/2502.05171
Wait I thought the router ends up specializing the experts?
Like there is no explicit goal aside from each 'expert' getting roughly equal weight?
And it happens that when you train the router you do end up passing certain classes of problem to each expert - just as a training result nothing as clean as a python expert. But math vs creative writing will tend to rely on different experts over the majority of the inference?
I do not know what I am talking about, this is my limited understanding...
2 replies →
> You can think of it like sharing.
was this meant to read "sharding"?
Are people getting decent tokens/second throughput? Some of these demos crawl at 1 tok/s or worse, which limits their utility.
yes, I average 80-120 tok/s on my RTX 3080 with gemma 4 and faster with Qwen 3.5. The main use-case here is just code-monkey agents. I'm not looking for architectural guidance, but an agent to take a spec and complete it.
And is this using conventional model loading (all in VRAM), or are you streaming it in some way?
in theory, QWEN3.6-35B-A3B should run as fast as a 3B model; and in practice, it can be just as dumb.