Mixture of Experts

Mixture of Experts (MoE) is a neural network architecture where many specialized "expert" sub-networks share the work of processing input — with a routing layer determining which experts handle each token. Only a small subset of experts activates per token, yielding efficient inference compared to a dense model with the same total parameter count. The result is models that are large in total parameters (capturing diverse knowledge) but cheap to run per token (only a fraction active at any time). Famous MoE deployments include Mixtral 8x7B (8 experts of 7B each, 2 active per token), Mixtral 8x22B, Gemini 1.5/2.5 (MoE in production at Google), DeepSeek V3 (671B total, 37B active), DBRX (132B/36B), and various other frontier models. MoE has become standard architecture for large frontier models because it enables scaling without proportional inference cost increases. AI governance, AI compliance, and AI risk management programs document architecture choices in deployment evidence — supporting responsible AI reproducibility across MoE-based enterprise AI environments at scale.

Centralpoint Routes Across Dense and MoE Models: Oxcyon's Centralpoint AI Governance Platform brokers MoE models (Mixtral, DBRX, DeepSeek) alongside dense models from OpenAI, Gemini, Claude, Llama, and embedded options. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds chatbots into your portals via one JavaScript line.


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