NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval
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.
Key Takeaways
- 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.
- Nemotron 3 Embed Model Usability and Deployment Matrix.
RTEB Multilingual Leaderboard screenshot (July 15, 2026) showing Nemotron-3-Embed-8B-BF16 ranked as #1.
- Multilingual & Code Retrieval: Supports retrieval across global enterprise data, technical documentation and multi-file code repositories.
NVIDIA NVFP4 Efficiency: Provides a Blackwell-optimized 4-bit deployment path for high-throughput retrieval with a smaller memory footprint.
- Day-0 Ecosystem Integration: Available immediately on Hugging Face , deployable as NVIDIA NIM microservice, supported by vLLM, and accessible through leading AI Cloud and inference partners.
Evaluation: Retrieval Quality, Agentic Efficiency, and Deployment Tradeoffs We evaluate Nemotron 3 Embed across three dimensions: retrieval quality, downstream agentic efficiency, and deployment tradeoffs.
- We also tested these models across ViDoRe V3 Text, and MMTEB Retrieval and LongEmbed using average NDCG@10.
Stats & Key Facts
- #We also tested these models across ViDoRe V3 Text, and MMTEB Retrieval and LongEmbed using average NDCG@10.
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.
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. 32k Context Window: Supports retrieval over long documents, large code contexts, and multi-turn agent histories while reducing truncation.
Multilingual & Code Retrieval: Supports retrieval across global enterprise data, technical documentation and multi-file code repositories. NVIDIA NVFP4 Efficiency: Provides a Blackwell-optimized 4-bit deployment path for high-throughput retrieval with a smaller memory footprint. Fine-Tuning and Distillation Recipes: NVIDIA NeMo AutoModel recipes support domain adaptation and model compression for teams adapting retrieval models to their own data.
For more details please read the original article at Hugging Face.
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