NVIDIA
Explore
Models
Blueprints
GPUs
Docs
Terms of Use
Privacy Policy
Your Privacy Choices
Contact

Copyright © 2025 NVIDIA Corporation

Models

Deploy and scale models on your GPU infrastructure of choice with NVIDIA NIM inference microservices
Publisher
Use Case
NIM Type
Sorting by Most Recent

nvidiallama-3_2-nemoretriever-300m-embed-v2

Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

nvidiallama-3_2-nemoretriever-300m-embed-v1

Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

nvidiallama-3.2-nemoretriever-1b-vlm-embed-v1

Multimodal question-answer retrieval representing user queries as text and documents as images.

nvidianv-embedcode-7b-v1

The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.

nvidiallama-3.2-nv-embedqa-1b-v2

Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.

metaesm2-650m

Generates embeddings of proteins from their amino acid sequences.

nvidianv-dinov2

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

nvidianv-embedqa-e5-v5

English text embedding model for question-answering retrieval.

nvidianv-embedqa-mistral-7b-v2

Multilingual text question-answering retrieval, transforming textual information into dense vector representations.

nvidianvclip

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

nvidianv-embed-v1

Generates high-quality numerical embeddings from text inputs.

baaibge-m3

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

snowflakearctic-embed-l

Optimized community model for text embedding.