RULER is a benchmark that evaluates a model’s ability to find and retrieve relevant information from extremely long contexts without degradation in performance as context length increases.
Nemotron 3 Super achieves 91.75% on RULER, meaning it maintains high accuracy when searching through its full 1-million-token context window. Traditional models often suffer from ‘lost in the middle’ problems where information in the middle of long contexts is overlooked. Nemotron 3 Super overcomes this, allowing it to reliably retrieve facts and reasoning from anywhere in a massive document or conversation history.
This capability is essential for self-hosted RAG systems with Milvus. Your vector database retrieves candidate documents, and Nemotron 3 Super can process all of them together in a single pass without losing track of earlier sections. This eliminates the need for multiple retrieval rounds or document re-ranking, simplifying your pipeline while improving answer quality. Multimodal RAG with Milvus demonstrates advanced retrieval patterns that leverage long-context models.