NVIDIA’s Llama 3.2 NeMo Retriever Enhances Multimodal RAG Pipelines

NVIDIA has launched the Llama 3.2 NeMo Retriever Multimodal Embedding Model, a breakthrough in retrieval-augmented generation (RAG) pipelines. The model significantly improves efficiency and accuracy by seamlessly integrating visual and textual data processing. Designed to handle multimodal data—including images, video, and audio—it addresses longstanding challenges in traditional RAG systems, which have been largely text-centric.

Vision Language Models (VLMs) like Gemma 3, PaliGemma, and LLaVA-1.5 have paved the way for this advancement, enabling applications such as visual question-answering and multimodal search. Despite their progress, VLMs remain prone to hallucinations. NVIDIA's solution aims to mitigate these inaccuracies while streamlining complex text extraction processes.