# NVIDIA Open-Sources Nemotron 3 Embed Text Embedding Model Series

- Category: Flash (快讯)
- Published: 2026-07-17T11:54:00.000Z
- Source date: 2026-07-17
- HTML: https://www.88z.ai/en/news/flash-20260717-b33e2185a5544210
- Markdown: https://www.88z.ai/en/news/flash-20260717-b33e2185a5544210.md
- Other language: https://www.88z.ai/news/flash-20260717-b33e2185a5544210.md

NVIDIA has released the open-source Nemotron 3 Embed text embedding model series, optimized for retrieval-augmented generation and agent search. The series offers 8B and 1B parameter sizes, supporting a 32k context window and 34 languages. The flagship 8B model ranks first on the RTEB multilingual retrieval benchmark with a score of 78.5%, while the 1B version retains 95% retrieval accuracy while significantly reducing inference costs through pruning and distillation.

## At a glance

- The flagship 8B model leads the RTEB benchmark with a 78.5% score
- The 1B version retains 95% accuracy after optimization while lowering inference costs

## The update

NVIDIA has officially open-sourced the Nemotron 3 Embed text embedding model series, designed to enhance performance in retrieval-augmented generation (RAG) and agent search scenarios. The model family includes versions with 8 billion and 1 billion parameters, both supporting a context window of up to 32k and covering 34 major languages, enabling broad cross-language processing capabilities. In terms of performance, the flagship 8B model excels, ranking first on the RTEB multilingual retrieval benchmark with a score of 78.5%. For resource-constrained scenarios, the 1B version is optimized using pruning and distillation techniques. It significantly reduces inference costs while effectively retaining 95% of retrieval accuracy, offering developers an efficient and cost-effective option.

## Sources

- [Embed](https://www.88z.ai/flash/cite/flash-20260717-b33e2185a5544210) (2026-07-17)

This article is an AI-assisted synthesis by 88Z AI based on the public sources below. Product claims and availability are attributed to their publishers. It does not represent independent testing or first-hand reporting.
