---
title: "NVIDIA Nemotron-3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval"
source: "Hugging Face：Blog（RSS）"
source_tier: "t1"
url: "https://huggingface.co/blog/nvidia/nemotron-3-embed-wins-rteb"
permalink: "https://feed.astralor.com/items/itm_ff8c14b3821e60f138bcff50"
published: "2026-07-16T16:01:21.000Z"
score: 89
channel: "ai"
tags: ["Hugging Face","Nemotron-3 Embed","NVIDIA","RAG","RTEB","Agentic Retrieval"]
---

# NVIDIA Nemotron-3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval

## AI commentary

> NVIDIA's breakthrough in embedding models is timely. For teams developing RAG and AI agents, the open-sourced Nemotron-3 Embed provides the strongest retrieval foundation currently available, especially for scenarios requiring precise long-text retrieval. Developers should quickly try it on Hugging Face and evaluate its performance on their own data.

## At a glance

NVIDIA announces that its Nemotron-3 Embed model has achieved the #1 overall score on the Retrieval Task Evaluation Benchmark (RTEB), surpassing all existing embedding models. The model is specifically designed for Agentic Retrieval and performs exceptionally well across multiple retrieval sub-tasks, including question answering, fact verification, and conversational retrieval. NVIDIA achieved this breakthrough through innovative training strategies and an efficient architecture, providing a new foundation for building smarter RAG applications and AI agents.

## Key points

- Nemotron-3 Embed achieved #1 overall on RTEB, surpassing leading embedding models.
- Optimized for Agentic Retrieval to enhance RAG and AI agent performance.
- Demonstrates leading performance in QA, fact verification, and conversational retrieval sub-tasks.
- NVIDIA released model weights and training details to support community research.

## Full text

A Blog post by NVIDIA on Hugging Face

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