Mixture-of-experts (MoE) routes each token through selected expert networks: Scout uses 16 experts from 109B total params, activating only 17B per token for sparse, efficient computation.
Dense models activate all parameters on every token—processing 10M tokens means 10M × 70B multiplications. Scout's MoE architecture uses a gating network to select which experts are "relevant," creating sparse activation: 10M tokens × 17B active = drastically fewer multiplications while maintaining quality. This efficiency is why Scout handles 10M-token contexts practically—latency doesn't explode with context size. For Zilliz users, MoE means: retrieve massive context from Zilliz Cloud without worrying about Scout's inference speed. Your vector database can return 1000 documents, Scout processes them faster than dense models processing 100 documents.
The architecture also means Scout adapts to query complexity. Simple lookup queries route differently than complex synthesis. This adaptive routing is implicit—you don't control it—but it's why Scout's 10M context feels fast in practice despite the massive token count. With Zilliz Cloud's a search engine provider scaling, you can retrieve comprehensively (heavy on vector database) while Scout remains efficient (sparse on model side). This separation is powerful for enterprise RAG.
Related Resources
- What Is a Vector Database? — vector retrieval foundations
- Retrieval-Augmented Generation (RAG) — RAG efficiency patterns
- Vector Embeddings — embeddings for adaptive routing