Llama 4 Maverick supports 1M tokens with 128 experts (400B total params) for dense reasoning; Scout offers 10M tokens with 16 experts for retrieving massive knowledge bases.
Maverick is optimized for depth—processing large documents with sophisticated analysis—while Scout enables breadth—pulling from enormous repositories in one pass. For Milvus deployments, choose based on your bottleneck: if you need deep reasoning on moderately-sized context (regulatory filings, technical specifications, meeting transcripts), Maverick’s 128-expert routing delivers precise expert selection. If you’re retrieving thousands of documents simultaneously or running agentic loops where context grows with each step, Scout’s 10M window absorbs full knowledge bases without truncation.
Both run locally via open weights, giving you latency control and cost predictability. The mixture-of-experts architecture means neither loads all parameters: Maverick activates ~17B of 400B, Scout ~17B of 109B. With Milvus semantic search pre-filtering your context chunks, Maverick works well for tight, high-accuracy pipelines; Scout powers systems where quantity and completeness matter more than parameter density.
Related Resources
- Milvus Overview — vector database fundamentals
- Enhance RAG Performance — optimization strategies
- RAG with vLLM — serving Llama models efficiently