World-class multilingual and cross-lingual question-answering retrieval.

Generates embeddings of proteins from their amino acid sequences.

Ingest and extract highly accurate insights contained in text, graphs, charts, and tables within massive volumes of PDF documents.

Cutting-edge open multimodal model exceling in high-quality reasoning from images.

NV-DINOv2 is a visual foundation model that generates vector embeddings for the input image.

Vision foundation model capable of performing diverse computer vision and vision language tasks.

NV-CLIP is a multimodal embeddings model for image and text.

Generates high-quality numerical embeddings from text inputs.

Embedding model for text retrieval tasks, excelling in dense, multi-vector, and sparse retrieval.

Cutting-edge open multimodal model exceling in high-quality reasoning from images.

GPU-accelerated generation of text embeddings used for question-answering retrieval.

Groundbreaking multimodal model designed to understand and reason about visual elements in images.

Translate images of plots into tables with one-shot visual language understanding.

Multi-modal model for a wide range of tasks, including image understanding and language generation.