The models that find, rank, and read an agent's evidence just shipped open and commercially available, with no revenue gate attached. NVIDIA's own tests say better retrieval cut agentic token cost about 14%.
Lena ParkForkast mind
What an agent knows is decided before it answers. The embedding search that surfaces candidate documents, the reranker that orders them, and the parser that turns a page image into usable text all sit upstream of the model doing the reasoning. On October 21, NVIDIA announced new Nemotron Retriever models for those three jobs and published the weights behind them, open and commercially available: Nemotron 3.5 Embed in three checkpoints, Nemotron 3.5 Rerank, and Nemotron 3.5 Parse. The week’s headline model, Nemotron 3.5 Super VL, shipped six days earlier. These are the transaction layer beneath it: the machinery that decides which evidence reaches an agent’s context, and how many tokens the agent burns finding it.
The chronology is worth stating precisely, because the weights and the announcement are two different claims. Parse arrived on Hugging Face on October 17, per its model card’s release date. Embed and Rerank landed October 20 on their own cards. NVIDIA’s portfolio announcement and a technical article by five NVIDIA authors followed on October 21, describing the Nemotron Retriever collection, formerly Nemotron RAG, as “new open, commercially available models for embedding, reranking, and document extraction.”
The three jobs divide cleanly. Parse, at 631 million parameters, converts document images into structured text with layout classes, bounding boxes, and reading order. Embed turns text and page images (tables, charts, infographics, datasheets) into vectors that can be searched together. Rerank, an 8.38-billion-parameter cross-encoder, scores each query-and-document pair to reorder what retrieval returns. In the article’s own triage: “Parse for extracting structured information from documents, Embed for searching text and images, and Rerank for improving the ordering of retrieved results.” Serving a model and feeding it evidence are different jobs. llm-d 1.0 gave away the serving layer on October 20; this release opens the layer that decides what the served model gets to see, for retrieval-augmented generation and agentic search alike.
The efficiency claim arrives with a method attached. In NVIDIA’s own agent measurement, with the agent’s model, tools, task set, corpus, and execution policy held fixed while only the retrieval configuration changed, adding Nemotron 3.5 Rerank “reduces agentic token cost per query by approximately 14% with Nemotron 3.5 Embed 8B, and 13% with Nemotron 3.5 Embed 1B, while improving retrieval accuracy in both configurations.” That is NVIDIA-reported, measured on NVIDIA’s setup, and it points at the stake: retrieval quality is a token-cost lever, not just an accuracy dial.
The benchmark claims are NVIDIA-reported too, and the company stamps them as of October 20. The Embed 8B model card carries 61.17 nDCG@10 on ViDoRe v3.1, first among dense retrieval models; the distilled 1B retains 95.4% of that score. The Rerank model card carries 60.49 against Qwen-3-VL-Reranker-8B’s 59.17 on the ViDoRe v3.1 reranking leaderboard, launched with the MTEB team for exactly this comparison. The Parse model card carries 75.20 on ParseBench at 10.5 pages per second on one H100, first among models under a billion parameters, with NVIDIA’s own table putting its previous Nemotron Parse 2.0 at 63.91 on the same benchmark. NVIDIA also reports the quantized 1B embedding checkpoint reaching 27.1% higher throughput on GB200 while retaining 99.85% of retrieval accuracy, and its NIM serving path hitting 6,031 query requests per second against 2,099 for a comparable vLLM setup. Every one of these figures is the seller’s own.
The license is the part deployers will read twice. All three model cards state that use is governed by the OpenMDW License Agreement, version 1.1, with Apache 2.0 base components and an MIT tokenizer in Parse’s case, and that the models are “ready for commercial or non-commercial use.” OpenMDW-1.1 carries no revenue gate. That puts these checkpoints on the permissive side of the line this beat keeps tracking: the $20 million deployment gates that four labs attach to their open-weight models are absent here. The weights, data, and training recipes sit on Hugging Face; NVIDIA serves the models as NIM microservices on build.nvidia.com, and inference partners Baseten, DeepInfra, FriendliAI, and OpenRouter carry most of the lineup.
One more evidence class sits inside NVIDIA’s article: partner evaluations, also vendor-reported. Perplexity reports the reranker scored highest on average among the rerankers it tested on a private 72-task benchmark and says the two companies are in talks about deeper technical collaboration. Turbopuffer measured roughly 5% more relevant results at Recall@20 than the next best model on enterprise PDF search. Elastic is exposing the models through its Inference API. Fine-tuning recipes are published too, with NVIDIA reporting chart-and-infographic gains of 18% for the 1B embedding model and 3% for the 8B after one epoch.
For builders, the stack is now open at every layer this week touched: weights, engines, serving, and evidence. Super VL was the gift Berlin wrapped in policy; these models are the unglamorous part that makes the gift usable, and they arrive with no gate on growth. Whether that stays free is a separate question, since the rent in this market keeps moving to whichever layer still holds scarcity. For now, what an agent knows just became cheaper to get right.
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