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Wednesday, 21 October 2026
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Nemotron 3.5 Embed/Rerank/Parse

Change history

  1. 21 Oct 2026
    NVIDIA expands Nemotron with new retrieval, reranking and document-parsing models for AI agents
    ParameterBeforeNow
    LineupNemotron 3 Embed / Llama Nemotron Embed VL 1BNemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, Nemotron 3.5 Parse 631M
    ViDoRe v3.1 dense retrieval score52.23 nDCG@10 (Llama Nemotron Embed VL 1B)61.17 nDCG@10 (Nemotron 3.5 Embed 8B BF16), rank 1
    Reranking score59.17 nDCG@10 (Qwen-3-VL-Reranker-8B)60.49 nDCG@10 (Nemotron 3.5 Rerank 8B BF16)
    Agentic token costBaseline embedding-only retrieval~13-14% lower with reranking added, plus higher accuracy
    NVFP4 throughput101.3 docs/sec (1B BF16 on GB200)128.8 docs/sec (1B NVFP4), 99.85% of accuracy retained
    NIM serving throughput2,099 req/sec (vLLM)6,031 req/sec (NIM, RTX PRO 6000 Blackwell)

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Models· Important· 🧪 Worth Testing

NVIDIA expands Nemotron with new retrieval, reranking and document-parsing models for AI agents

NVIDIA has added three new open, commercially licensed components to its Nemotron Retriever lineup: Embed models (8B and 1B, in BF16 and NVFP4) for searching text and images, a Rerank model (8B) that reorders retrieved evidence by relevance, and a compact 631M Parse model that converts document images into structured text. The goal is to feed AI agents with better, more relevant evidence so they need fewer search rounds and fewer inference tokens to answer a question. NVIDIA reports the new Embed and Rerank models top the ViDoRe v3.1 leaderboards for dense retrieval and reranking respectively.

Hugging Face