NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval
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NVIDIA Nemotron 3 Embed Ranks #1 Overall on RTEB, Advancing Agentic Retrieval
Community Article Published<br>July 16, 2026
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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<br>Role<br>Best for
Nemotron-3-Embed-8B-BF16<br>Flagship Quality Anchor: The flagship embedding model, ranking #1 on RTEB.<br>Precision-critical retrieval and high-stakes enterprise RAG
Nemotron-3-Embed-1B-BF16<br>High-Efficiency Standard: A high-efficiency model for production retrieval where latency and cost matter.<br>Cost- and latency-sensitive production serving
Nemotron-3-Embed-1B-NVFP4<br>Hardware-Accelerated Variant: A Blackwell-optimized variant for high-throughput retrieval with a smaller memory footprint.<br>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.
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.
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. The 8B model establishes the model collection’s quality ceiling, while the 1B BF16 and NVFP4 variants bring the same retrieval-focused design to lower-cost and higher-throughput deployment settings.
RTEB Leadership and Strong Gains Across Retrieval Benchmarks
We first evaluated the models on RTEB, where Nemotron-3-Embed-8B-BF16 ranks #1. We also tested these models across ViDoRe V3 Text, and MMTEB Retrieval and LongEmbed using average NDCG@10.
Figure 2. Retrieval accuracy using average NDCG@10 across RTEB, ViDoRe V3 Text, MMTEB Retrieval and LongEmbed, comparing the Nemotron 3 Embed models with prior-generation Nemotron baselines.
Nemotron-3-Embed-8B-BF16 ranks #1 on RTEB, scoring 78.5% on RTEB and 75.5% on MMTEB Retrieval.
Nemotron-3-Embed-1B-BF16 brings much of the 8B model’s retrieval quality into a smaller deployment footprint. It scores 72.4% on RTEB, reducing error rate by 27% over its 1B predecessor (llama-nemotron-embed-vl-1b-v2), and scores 71.0% on MMTEB Retrieval, reducing error rate by 28%.
Why Better Retrieval Matters for Agents
To evaluate retrieval in an agentic setting, we use a search agent powered by Nemotron 3 Ultra and vary the embedding model used by the retrieval system. Better retrieval can return relevant evidence earlier, helping the agent avoid repeated searches, unnecessary reasoning turns, and extra context inspection. We compare...