Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
Efficient multimodal model excelling at multilingual tasks, image understanding, and fast-responses
Powerful, multimodal language model designed for enterprise applications, including software development, data analysis, and reasoning.
A general purpose multimodal, multilingual 128 MoE model with 17B parameters.
A multimodal, multilingual 16 MoE model with 17B parameters.
Build artificial general agents (AGA) powered by AGI models that continuously process and synthesize multimodal enterprise data, enabling reasoning, planning, and refinement to generate comprehensive reports.
The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.
Cutting-edge open multimodal model exceling in high-quality reasoning from images.
Cutting-edge open multimodal model exceling in high-quality reasoning from image and audio inputs.
Connect AI applications to multimodal enterprise data with a scalable retrieval augmented generation (RAG) pipeline built on highly performant, industry-leading NIM microservices, for faster PDF data extraction and more accurate information retrieval.
Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
Cutting-edge open multimodal model exceling in high-quality reasoning from images.
Vision foundation model capable of performing diverse computer vision and vision language tasks.
English text embedding model for question-answering retrieval.
Multilingual text question-answering retrieval, transforming textual information into dense vector representations.
Generates high-quality numerical embeddings from text inputs.
Cutting-edge open multimodal model exceling in high-quality reasoning from images.
Optimized community model for text embedding.
GPU-accelerated generation of text embeddings used for question-answering retrieval.