Mixture-of-experts (MoE) routes each token through specialized expert networks: Scout uses 16 experts from 109B params, activating only 17B per forward pass.
Traditional dense models like Llama 3 activate all 70B parameters on every token. Scout’s MoE approach uses a gating network to select which 16 experts are “relevant” for each token, creating a sparse activation pattern. This reduces compute by 4x while maintaining quality, making Scout practical for long-context RAG where token volume explodes. A 10M-token context in a dense model would require 700B multiplications; Scout’s sparse routing drops this dramatically.
For Milvus workflows, MoE matters because retrieval results vary in complexity. Simple lookup queries route through different experts than complex synthesis tasks. Scout adapts expert selection to query difficulty, similar to how Milvus uses metadata filtering to accelerate vector search. The result is faster inference on long contexts—critical when your Milvus retrieval returns thousands of semantically-similar chunks that Scout must synthesize into a coherent answer.
Related Resources
- Milvus Quickstart — build RAG pipelines
- Agentic RAG with LangGraph — handle complex queries
- Enhance RAG Performance — optimization techniques