Llama 4 Scout is Meta’s 17B-parameter mixture-of-experts model with 10M token context, enabling massive knowledge base retrieval in RAG pipelines.
Released April 2025, Scout processes 10M tokens—roughly 7 million words—in a single pass, making it ideal for RAG systems that need to retrieve and process entire document collections. Its mixture-of-experts (MoE) architecture routes queries through 16 specialized experts from a 109B total parameter pool, delivering dense reasoning on long documents while keeping active memory efficient. The open-weight design means you can run Scout locally without API costs, fine-tune it for your domain, and maintain full data privacy.
With Milvus, Scout excels at agentic RAG workflows: your vector database retrieves relevant context chunks, Scout processes the full context with 10M-token awareness, and the model grounds responses in your exact knowledge base. This combination eliminates truncation errors and hallucinations from token limits—critical for enterprise document Q&A, legal contract analysis, and research synthesis where missing context leads to wrong answers.
Related Resources
- Milvus Quickstart — get Milvus running in minutes
- Agentic RAG with Milvus and LangGraph — production agentic retrieval
- RAG with Milvus and LlamaIndex — LlamaIndex integration guide
- Milvus Performance Benchmarks — speed and scale metrics