NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Back to Articles a]:hidden"> NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval Enterprise + Article Published July 16, 2026 Upvote 23 +17 Yauhen Babakhin ybabakhin Follow nvidia Ronay Ak ronay-nv Follow nvidia Jiarui Cai jiaruic Follow nvidia Vinay Raman viraman Follow nvidia Radek Osmulski radekosmulski-nvidia Follow nvidia Jakub Zakrzewski jzakrzewski Follow nvidia Anmol Gupta anmolg-nvidia Follow nvidia Oliver Holworthy oliverholworthy Follow nvidia Sahel Sharifymoghaddam sahel-sh Follow nvidia Khang Pham KhangPhamML Follow nvidia James Rong hrong-nv Follow nvidia Steve Han steve-nvidia Follow nvidia Sean Sodha ssodha-nv Follow nvidia Isabel Hulseman ihulseman0220 Follow nvidia Bo Liu BoLiu Follow nvidia Evaluation: Retrieval Quality, Agentic Efficiency, and Deployment Tradeoffs RTEB Leadership and Strong Gains Across Retrieval Benchmarks Why Better Retrieval Matters for Agents Scaling Retrieval with NVFP4 on Blackwell Day 0 Performant NIM How We Built the Nemotron 3 Embed Models Scaling Down to 1B Enterprise Partner Evaluations Getting Started Retrieval is critical in multi-step agentic workflows where poor retrieval can cause agents to fetch irrelevant context, re-query, waste token budget, and carry noise into later reasoning steps. Today, we are releasing NVIDIA Nemotron 3 Embed , a collection of open and commercially available embedding models designed to improve retrieval quality while giving developers practical deployment options for production-scale RAG, agentic retrieval, code retrieval, and agent memory. The collection includes three open models that achieve state-of-the-art retrieval across the accuracy-efficiency curve, led by an 8B model that tops the RTEB leaderboard and efficient 1B variants built for production-scale deployment: Model Role Best for Nemotron-3-Embed-8B-BF16 Flagship Quality Anchor: The flagship embedding model, ranking #1 on RTEB. Precision-critical retrieval and high-stakes enterprise RAG Nemotron-3-Embed-1B-BF16 High-Efficiency Standard: A high-efficiency model for production retrieval where latency and cost matter. Cost- and latency-sensitive production serving Nemotron-3-Embed-1B-NVFP4 Hardware-Accelerated Variant: A Blackwell-optimized variant for high-throughput retrieval with a smaller memory footprint. Ultra-high-throughput and massive-scale infrastructure Table 1.
Nemotron 3 Embed Model Usability and Deployment Matrix. Figure 1. RTEB Multilingual Leaderboard screenshot (July 15, 2026) showing Nemotron-3-Embed-8B-BF16 ranked as #1. Key Features Beyond the RTEB result, Nemotron 3 Embed introduces a production-ready feature set for enterprise retrieval deployments: Open Weights, Datasets, and Recipes: Gives teams control to inspect, tune, fine-tune, and deploy retrieval models on their own infrastructure.
Source: https://huggingface.co/blog/nvidia/nemotron-3-embed-wins-rteb
Nemotron 3 Embed Model Usability and Deployment Matrix. Figure 1. RTEB Multilingual Leaderboard screenshot (July 15, 2026) showing Nemotron-3-Embed-8B-BF16 ranked as #1. Key Features Beyond the RTEB result, Nemotron 3 Embed introduces a production-ready feature set for enterprise retrieval deployments: Open Weights, Datasets, and Recipes: Gives teams control to inspect, tune, fine-tune, and deploy retrieval models on their own infrastructure.
Source: https://huggingface.co/blog/nvidia/nemotron-3-embed-wins-rteb