Nemotron ColEmbed V2: Raising the Bar for Multimodal Retrieval with ViDoRe V3’s Top Model

Back to Articles Nemotron ColEmbed V2: Raising the Bar for Multimodal Retrieval with ViDoRe V3’s Top Model Enterprise + Article Published February 4, 2026 Upvote 26 +20 Ronay Ak ronay-nv Follow nvidia Gabriel de Souza Pereira Moreira gmoreira-nv Follow nvidia Modern search systems are increasingly designed to process heterogeneous document images that may contain text, tables, charts, figures, and other visual components. In this context, accurately retrieving relevant information across these diverse modalities is a central challenge. Multimodal embedding models built on top of foundational vision–language models (VLMs) map diverse content types into a shared representation space, enabling unified retrieval over text, images, and structured visual elements. Although encoding an entire query and candidate document into a single vector is a common practice—exemplified by our recently released commercial-ready Llama-Nemotron-Embed-VL-1B which prioritizes efficiency and low storage—there is an increasing research direction on multi-vector, late-interaction style embedding architectures which provide fine-grained multi-vector interaction between queries and documents. By enabling richer token representations, these models better capture more detailed semantic relationships, and they have shown higher accuracy performance on various (multimodal) benchmarks. NVIDIA introduces the Nemotron ColEmbed V2 family, a set of late-interaction embedding models available in three sizes—3B,...