# NVIDIA's Nemotron-3-Embed 8B tops RTEB retrieval test

> The 8-billion-parameter text embedding model claims the number one overall spot on the RTEB benchmark, with an eye toward agentic retrieval.

Published by The Open Weights on Jul 16, 2026. Canonical: https://theopenweights.com/news/nemotron-3-embed-8b-qagw

## Key facts

- Company: NVIDIA
- Model: Nemotron-3-Embed 8B
- Version: 3
- Category: Embeddings
- Modalities: Embeddings
- License: Other (Open weights, commercial use allowed)
- Parameters: 8B
- Architecture: Ministral3Model
- Precision: BF16
- Weights size: 15.9 GB
- Significance: notable
- Published: 2026-07-16
- Last verified: 2026-07-23
- Hugging Face: https://huggingface.co/nvidia/Nemotron-3-Embed-8B-BF16
- Canonical URL: https://theopenweights.com/news/nemotron-3-embed-8b-qagw

NVIDIA has released Nemotron-3-Embed 8B, a text-embedding model that the company says ranks first overall on RTEB, a retrieval benchmark used to measure how well models turn text into vectors for search and reranking. The model is available on Hugging Face in a BF16 build at `nvidia/Nemotron-3-Embed-8B-BF16`.

Embedding models are the quiet workhorses behind modern retrieval systems. They convert documents and queries into numerical representations so that a system can find the most relevant passages by measuring similarity. Better embeddings mean fewer irrelevant hits and more grounded answers, which is why retrieval quality has become a competitive battleground.

## Why it matters

NVIDIA frames the release around what it calls agentic retrieval — the idea that autonomous AI agents increasingly depend on fetching accurate context across many steps of a task. A strong embedding model is the foundation of retrieval-augmented generation (RAG) pipelines, and a top RTEB placement is a signal that Nemotron-3-Embed could slot in as a drop-in upgrade for teams building search and agent workflows.

A few practical notes from the [announcement](https://huggingface.co/blog/nvidia/nemotron-3-embed-wins-rteb):

- The model carries 8 billion parameters and is offered in BF16 precision.
- NVIDIA reports a #1 overall standing on the RTEB retrieval benchmark.
- It is positioned specifically for agentic and RAG-style retrieval use cases.

As always, benchmark leadership is a snapshot rather than a guarantee, and rankings shift as new models arrive. The model is distributed under a custom NVIDIA license, so teams should review the terms before deploying it in production. Still, for developers already invested in NVIDIA's Nemotron ecosystem, a competitive embedding option rounds out the retrieval stack.

## Get the model

- [Hugging Face](https://huggingface.co/nvidia/Nemotron-3-Embed-8B-BF16)
- [Announcement](https://huggingface.co/blog/nvidia/nemotron-3-embed-wins-rteb)

## Sources

- [NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval](https://huggingface.co/blog/nvidia/nemotron-3-embed-wins-rteb) — Announcement, Jul 16, 2026
- [nvidia/Nemotron-3-Embed-8B-BF16](https://huggingface.co/nvidia/Nemotron-3-Embed-8B-BF16) — Hugging Face, Jul 14, 2026

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "NVIDIA's Nemotron-3-Embed 8B tops RTEB retrieval test", The Open Weights, Jul 16, 2026, https://theopenweights.com/news/nemotron-3-embed-8b-qagw