You have to look at the size of each expert; Kimi's has about 50G parameters while GLM's has 40G. The number of the experts tells you about the diversity of its skills.
Yes, this part is accurate. Expert density determines how much raw compute each hidden state gets.
> The number of the experts tells you about the diversity of its skills.
Most people misunderstand this part. Counter-intuitively experts don't develop diverse skills, they instead balance compute during the forward pass, allowing models to increase their parameter count without the MLP layers exploding in memory + compute requirements.
Yeah, "experts" is a ML/research word for this (MoE was first published in 1991; and has been around for a long time, it even predates deep learning). it's not the everyday/colloquial meaning of 'expert'.
Yes, this part is accurate. Expert density determines how much raw compute each hidden state gets.
> The number of the experts tells you about the diversity of its skills.
Most people misunderstand this part. Counter-intuitively experts don't develop diverse skills, they instead balance compute during the forward pass, allowing models to increase their parameter count without the MLP layers exploding in memory + compute requirements.