{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Mixture of Experts — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Mixture of Experts (MoE)</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">An architecture where a model consists of many specialized subnetworks, called experts, and a routing mechanism that selectively activates only a subset of them for each input. MoE allows models to have a very large total number of parameters while keeping the computational cost of each forward pass manageable, and is a key technique behind some of the most capable and efficient large language models.</p><a href=\"https://gaks.ai/glossary/mixture-of-experts\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/mixture-of-experts →</a></div>"}