Aiconomy

Mixture of Experts (MoE)

A neural network architecture that routes each input to only a subset of specialized 'expert' sub-networks, enabling much larger models without proportionally increasing compute costs.

MoE architectures allow models with trillions of total parameters while only activating a fraction for each input. GPT-4 is widely reported to use a MoE architecture with multiple expert networks. Mistral's Mixtral 8x7B model activates only 2 of 8 experts per token, achieving performance comparable to models 3x its active size. Google's Switch Transformer scaled to 1.6 trillion parameters using MoE. The approach is key to reducing inference costs — a critical factor as AI scales to billions of daily queries.

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