Model Overview

Description:

Nemotron-3-Embed-1B-BF16 is a versatile text embedding model trained by NVIDIA and optimized for retrieval and semantic similarity tasks. It provides strong multilingual and cross-lingual retrieval capabilities and is designed to serve as a foundational component in text-based Retrieval-Augmented Generation (RAG) systems. This model was evaluated on 34 languages: English, Arabic, Assamese, Bengali, Bulgarian, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hinglish, Indonesian, Italian, Japanese, Korean, Malay, Marathi, Nepalese, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Ukrainian, Urdu, Vietnamese.

The model generates dense vector embeddings from multilingual text inputs, enabling retrieval, semantic search, and (agentic) RAG workflows. As a core component of text retrieval systems, an embedding model transforms text, such as questions or passages, into dense vector representations. These models are typically transformer encoders that process input tokens and produce embeddings suitable for efficient similarity matching.

Among models of comparable size, Nemotron-3-Embed-1B-BF16 achieves state-of-the-art performance across multiple multilingual retrieval benchmarks.

This model is ready for commercial use.

License/Terms of Use:

GOVERNING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). Additional Information: Built with Ministral-3-3B-Instruct-2512 which is released under Apache 2.0.

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

Model Developer: NVIDIA

Deployment Geography:

Global

Use Case:

The Nemotron-3-Embed-1B-BF16 is most suitable for users who want to build a multilingual question-and-answer application over a large text corpus, leveraging the latest dense retrieval technologies, including RAG pipelines.

Release Date:

07/16/2026 via Build.NVIDIA.com

Model Architecture:

Architecture Type: Transformer

Network Architecture: Ministral-3-3B-Instruct-2512 based pruned model

Embedding Dimension: 2048

Max Sequence Length: 32768

Number of Model Parameters: ~1.14B

Precision: bf16

The Nemotron-3-Embed-1B-BF16 was derived from the Nemotron-3-Embed-3B text-embedding model through two iterative rounds of structured pruning and distillation, using NVIDIA ModelOpt mcore_minitron Neural Architecture Search (NAS).

Input(s):

Input Type(s): Text

Input Format(s): String / List of strings

Input Parameters: One Dimensional (1D)

Other Properties Related to Input: Text inputs longer than the maximum context length of 32768 tokens should be truncated or chunked.

Output(s):

Output Type(s): Floats (dense vector embeddings)

Output Format(s): List of floats

Output Parameters: One Dimensional (1D)

Other Properties Related to Output: The model outputs a 2048-dimensional embedding vector for each input text string.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engines: Rust + CUDA

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere

NVIDIA Blackwell

NVIDIA Hopper

NVIDIA Lovelace

Preferred/Supported Operating System(s): Linux

Supported GPU SKUs: NVIDIA H100 80GB HBM3, NVIDIA A100 SXM4 80GB, NVIDIA L40S, NVIDIA A10G, NVIDIA GB200, NVIDIA RTX PRO 6000 Blackwell Server Edition

Performance:

Avg. NDCG@10 on text retrieval benchmarks (chunk retrieval), evaluated at sequence length 4096.

Model Version(s):

Nemotron-3-Embed-1B-BF16

Short Name: nemotron-3-embed-1b

Training, Testing, and Evaluation Datasets:

Dataset Overview:

Total Size: 8.5M+ data samples

Total Number of Datasets: 161 dataset files

Dataset Partition: Training [100%], Testing [N/A — evaluation benchmarks used separately], Validation [N/A — evaluation benchmarks used separately].

Model distillation training was conducted using publicly available, commercially permissible datasets and synthetically generated datasets. Synthetic data was created either by generating queries from seed documents or by generating complete question–answer pairs through LLM-based prompting.

Public Datasets:

Synthetic Datasets:

Synthetic query-document pairs were generated either from scratch or by using seed datasets to generate queries with the models listed below.

Training Dataset:

Data Modality: Text

Training Data Size: 8.5M+

Data Collection Method by dataset: Hybrid: Human, Automated, Synthetic

Labeling Method by dataset: Hybrid: Human, Automated, Synthetic

Properties: Model training was conducted on text datasets using question–passage pairs from publicly available, commercially permissible datasets and synthetically generated datasets.

Testing Dataset:

Properties: Not Applicable. Model quality was assessed using the evaluation benchmark datasets described in the Evaluation Dataset subsection.

Data Collection Method by dataset: Not Applicable

Labeling Method by dataset: Not Applicable

Evaluation Dataset:

Data Collection Method by dataset: Hybrid: Human, Automated, Synthetic

Labeling Method by dataset: Hybrid: Human, Automated, Synthetic

Properties: This model is evaluated on 16 public tasks on Retrieval Embedding Benchmark (RTEB), a benchmark designed to reliably evaluate the retrieval accuracy of embedding models for real-world applications. More details on RTEB can be found on their leaderboard.

The model was also evaluated on the MMTEB (Retrieval) benchmark datasets (paper), and on the eight text datasets (extracted via OCR) from ViDoRe-V3 benchmark.

Inference:

Acceleration Engine: Rust + CUDA

Test Hardware: NVIDIA Lovelace (L40S)

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case, and address unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.

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