Mixture of Experts
Mixture of Experts (MoE) is a neural network design that scales model parameters without increasing compute cost. Instead of activating the entire network for every token, MoE routes inputs to specialized sub-networks ("experts") using a gating router.
Frequently Asked Questions
What is active parameter count vs. total parameter count in MoE?▼
Total parameter count is the size of all experts combined. Active parameter count is the size of only the experts triggered for a specific token (e.g. 2 out of 8 experts).
Why is MoE beneficial?▼
It allows models to achieve the reasoning capabilities of massive models while running inference with the speed and cost of much smaller models.
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