NVIDIA-Nemotron-Nano-12B-v2-Base

Model Developer: NVIDIA Corporation

Model Dates:

June 2025 - August 2025

Data Freshness:

May 1, 2025

The pretraining data has a cutoff date of May 1, 2025.

Model Overview

Description

NVIDIA-Nemotron-Nano-12B-v2-Base is a large language model (LLM) developed by NVIDIA that is designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers with just six Attention layers. The model features a context length of 128K. The supported languages include: English, Spanish, French, German, Japanese, Italian, Portuguese, Chinese, Arabic, Danish, Korean, Dutch, Polish, Russian, Swedish, and Thai. Improved using Qwen.

This model is ready for commercial use.

Feature Voting

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License/Terms of Use

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement.

Deployment Geography: Global

Use Case

This model is intended for developers and researchers building LLMs.

Release Date: 08/18/2025

Hugging Face 08/18/2025 via https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base

Reference(s)

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

Model Architecture

• Architecture Type: Mamba2-Transformer Hybrid

• Network Architecture: Nemotron-Hybrid

• Number of model parameters: 12.31B

Model design

The model was trained with 20T tokens, with a batch size of 736, and used the Warmup-Stable-Decay (WSD) learning rate schedule with 8B tokens of learning rate warm up, peak learning rate of 4.5e-4 and minimum learning rate of 4.5e-6. There are a total of 62 layers, of which there are 28 of each MLP and Mamba-2, the remaining layers use GQA with 8 groups

Input

• Input Type(s): Text

• Input Format(s): String

• Input Parameters: One-Dimensional (1D): Sequences

• Maximum input size: 128K tokens

• Other Properties Related to Input: Supported languages include English, Spanish, French, German, Japanese, Italian, Portuguese, Chinese, Arabic, Danish, Korean, Dutch, Polish, Russian, Swedish, Thai.

Output

• Output Type(s): Text

• Output Format: String

• Output Parameters: One-Dimensional (1D): Sequences

• Maximum output size: 128K tokens

Our AI models are designed and 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 Engine(s): NeMo 25.07.nemotron-nano-v2
• Supported Hardware Microarchitecture Compatibility: NVIDIA H100-80GB, NVIDIA A100
• Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

• v1.0

Training, Testing, and Evaluation Datasets:

NVIDIA-Nemotron-Nano-12B-v2-Base is pre-trained on a large corpus of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 15 multilingual languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was trained for approximately twenty trillion tokens.

Alongside the model, we release our final pretraining data, as outlined in this section. For ease of analysis, there is a sample set that is ungated. For all remaining code, math and multilingual data, gating and approval is required, and the dataset is permissively licensed for model training purposes

Data Modality: Text The total size: 10,648,823,153,919 Tokens Total number of datasets: 141 Dataset partition: Training [100%], testing [0%], validation [0%]
Time period for training data collection: 2013 to May 1, 2025
Time period for testing data collection: 2013 to May 1, 2025
Time period for validation data collection: 2013 to May 1, 2025

More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model.

Private Non-publicly Accessible Datasets of Third Parties

Crawled and Scraped from Online Sources by NVIDIA

The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways described in the Nemotron-CC paper. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.

The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set (for additional details, refer to the technical report).

NVIDIA-Sourced Synthetic Datasets

Training Dataset :

We use a considerable amount of synthetic data. Out of 10.6 trillion tokens, 3,534,013,958,278 tokens are synthetically generated.

We extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC. Additionally, we used data from Wikipedia and FineWeb-2 (Penedo et al., 2025) for these fifteen languages.

We collect a total of 922,476,782,017 tokens of code in 43 different languages.

Evaluation Dataset:

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

Base Benchmark Evaluations

We evaluated our model on the following benchmarks:

Table 1: Accuracy of Nemotron-Nano-V2-Base models versus existing SoTA models. N-Nano-V2 is short for Nemotron-Nano-V2. The distilled N-Nano-V2-9B-Base is compared against Qwen3-8B-Base and Gemma3-12B-Base, and the best score is highlighted in each row.

Table 2: Accuracy of Nemotron-Nano-V2-Base models versus existing SoTA models on multilingual benchmarks. N-Nano-V2 is short for Nemotron-Nano-V2. The distilled N-Nano-V2-9B-Base is compared against Qwen3-8B-Base and Gemma3-12B-Base, and the best score is highlighted in each row.

Inference

• Engines: HF, vLLM, TRT-LLM

• Test Hardware NVIDIA A100 80GB, H100 80GB

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 Trustworthy AI terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses 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.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Citation

@misc{nvidia2025nvidianemotronnano2,
      title={NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model},
      author={NVIDIA},
      year={2025},
      eprint={2508.14444},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.14444},
}