Mixture of Experts (MoE)

Mixture of Experts is a neural network architecture where multiple specialized sub-networks—called experts—each handle different types of inputs, with a gating mechanism routing each input to the most relevant expert or combination of experts. This approach allows models to scale efficiently by activating only a subset of parameters for each input, rather than using all parameters for every computation. MoE architectures have enabled the creation of extremely large language models like Mixtral 8x7B and newer open-source alternatives that maintain competitive performance while reducing computational costs. In Web3, MoE-based models are increasingly explored for building efficient on-chain AI agents and reducing inference costs for decentralized AI applications. Example: Mixtral 8x7B, created by Mistral AI, uses a MoE architecture with eight expert sub-networks; a gating layer selects the two most relevant experts for each token, achieving GPT-4-level performance with a smaller computational footprint suitable for resource-constrained blockchain environments. Why it matters for AI and data in Web3: MoE architectures enable efficient, cost-effective AI inference critical for decentralized systems where computational resources are limited and expensive. By activating only necessary experts per query, MoE models reduce the overhead of running AI agents on blockchain networks, making sophisticated AI capabilities economically viable in Web3 applications.

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