{"data":{"slug":"moe","etype":"concept","name":"Mixture of Experts (MoE)","cat":"Core Technical","subtitle":"Huge capacity, small bills: only a few experts fire per token.","summary":"An MoE model holds many expert sub-networks but routes each token through only a few — DeepSeek-V3 stores 671B parameters yet computes with 37B per token. That decouples capacity from cost and is how open labs reached frontier scale on constrained hardware. The trade: all those parameters still occupy memory, which pushes HBM demand even higher — an architecture choice you can read directly in the memory market.","attrs":null,"kf":null,"aliases":["mixture of experts","sparse models"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":null,"region":null,"asof":"2026-01-15","sources":[{"url":"https://arxiv.org/abs/2101.03961","label":"Switch Transformers paper"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T08:56:05+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}