NVIDIA Nemotron
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Open, Production-ready Enterprise Models. Nvidia Open Model license.
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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.
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
Computational load
Cumulative compute : 1.45E+24 FLOPS
Estimate energy and emissions for model training: 708.3 MWh
| # of tokens | Compute [FLOPS] | Energy [MWh] | |
|---|---|---|---|
| 12B Base Pre-training | 20T | 1.45E+24 | 708.3 |
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
| Dataset |
|---|
| Global Regulation |
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).
| Dataset | Modality | Dataset Size | Collection Period | Collecting Organisation |
|---|---|---|---|---|
| English Common Crawl | Text | 3.36T | 4/8/2025 | NVIDIA Advanced Deep Learning Research |
| Multilingual Common Crawl | Text | 812.7B | 5/1/2025 | NVIDIA Advanced Deep Learning Research |
| GitHub Crawl | Text | 747.4B | 4/29/2025 | NVIDIA Advanced Deep Learning Research |
NVIDIA-Sourced Synthetic Datasets
| Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation |
|---|---|---|---|---|
| Synthetic Art of Problem Solving from DeepSeek-R1 | Text | 40086030608 | Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10; | DeepSeek-R1 |
| Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1 | Text | 327M | social-chemestry-101; Moral Stories | Mixtral-8x22B-v0.1 |
| Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 83.6M | OpenStax - CC BY-SA subset | DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B |
| Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 9.7M | OpenStax - CC BY-SA subset | DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B |
| Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B | Text | 175M | OpenStax - CC BY-SA subset; GSM8K; Open Textbook Library - CC BY-SA & GNU subset | DeepSeek-R1, DeepSeek-V3; DeepSeek-V3-0324; Qwen2.5-72B |
| Nemotron-PrismMath | Text | 4.6B | Big-Math-RL-Verified; OpenR1-Math-220k | Qwen2.5-0.5B-instruct, Qwen2.5-72B-Instruct; DeepSeek-R1-Distill-Qwen-32B |
| Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | 350M | arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD | Qwen2.5-72B-Instruct |
| Refreshed Nemotron-MIND from phi-4 | Text | 73B | Common Crawl | phi-4 |
| nv-cc-math-45-jun2025 | Text | 52.3B | Common Crawl | DeepSeek-V3 |
| nv-cc-math-3-jun2025 | Text | 80.9B | Common Crawl | phi-4 |
| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324 | Text | 4.0B | AQUA-RAT; LogiQA; AR-LSAT | DeepSeek-V3; DeepSeek-V3-0324 |
| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B | Text | 4.2B | AQUA-RAT; LogiQA; AR-LSAT | Qwen3-30B-A3B |
| Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct | Text | Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10; GSM8K; PRM800K | Qwen2.5-32B-Instruct; Qwen2.5-Math-72B; Qwen2.5-Math-7B; Qwen2.5-72B-Instruct | |
| Synthetic MMLU Auxiliary Train from DeepSeek-R1 | Text | 0.5B | MMLU Auxiliary Train | DeepSeek-R1 |
| Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD | Qwen2.5-72B-Instruct | |
| Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct | Text | 415.8B | Common Crawl | Qwen3-30B-A3B; Mistral-NeMo-12B-Instruct |
| Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B | Text | Common Crawl | Qwen3-30B-A3B | |
| Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B | Text | Wikimedia | Qwen3-30B-A3B | |
| Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct | Text | - | Nemotron-4-340B-Instruct |
Training Dataset :
| Dataset | # Tokens |
|---|---|
| English Common Crawl | 3,360,110,334,818 |
| English Synthetic CC | 1,949,464,641,123 |
| Crawl++ | 360,389,153,262 |
| Math | 124,606,230,663 |
| Synthetic Math | 73,007,767,155 |
| Code | 747,409,228,724 |
| Synthetic Code | 175,067,553,293 |
| English Wiki | 17,349,266,926 |
| Books | 0 |
| Papers | 191,586,493,365 |
| PDF-to-text | 141,096,578,533 |
| Code SFT | 60,025,726,817 |
| STEM SFT | 272,680,426,295 |
| General SFT | 6,057,478,645 |
| Multilingual | 2,172,261,909,350 |
| Synthetic multilingual | 997,710,364,950 |
| Total | 10,648,823,153,919 |
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.
| Language | Total Tokens |
|---|---|
| Arabic | 118,056,362,726 |
| Danish | 117,747,321,618 |
| German | 146,613,691,781 |
| Spanish | 469,156,575,409 |
| French | 139,982,002,289 |
| Italian | 298,858,370,174 |
| Japanese | 682,755,693,336 |
| Korean | 127,099,747,538 |
| Dutch | 89,041,592,681 |
| Polish | 105,356,493,147 |
| Portuguese | 243,249,275,089 |
| Russian | 185,314,014,057 |
| Swedish | 74,954,953,299 |
| Thai | 160,778,944,467 |
| Chinese | 211,007,236,689 |
We collect a total of 922,476,782,017 tokens of code in 43 different languages.
| Language | Tokens |
|---|---|
| Assembly | 750,628,764 |
| C | 42,657,300,868 |
| C# | 56,153,329,307 |
| C++ | 67,773,701,658 |
| CommonLisp | 263,234,672 |
| CSS | 38,848,760,035 |
| Cuda | 400,222,993 |
| Dart | 3,816,960,470 |
| Dockerfile | 474,958,084 |
| Fortran | 1,105,049,387 |
| Go | 8,332,419,480 |
| Haskell | 1,294,613,669 |
| HTML | 69,082,117,487 |
| Java | 131,440,465,822 |
| JavaScript | 75,573,420,861 |
| JSON | 15,366,881,241 |
| Julia | 621,046,949 |
| JupyterNotebook | 2,241,893,197 |
| Lua | 4,146,420,802 |
| Makefile | 12,640,010,879 |
| Markdown | 64,796,743,311 |
| Mathematica | 320,504,225 |
| OmniversePython | 26,946,093 |
| Pascal | 1,625,013,876 |
| Perl | 1,575,314,434 |
| PHP | 61,575,339,005 |
| Python | 126,916,727,384 |
| R | 19,811,381,935 |
| reStructuredText | 1,779,876,391 |
| Ruby | 6,446,962,615 |
| Rust | 4,438,640,533 |
| Scala | 3,343,959,154 |
| Shell | 18,758,779,250 |
| SQL | 23,205,633,085 |
| Swift | 5,976,714,881 |
| SystemVerilog | 233,056,185 |
| TeX | 7,347,157,527 |
| TypeScript | 15,657,838,582 |
| Verilog | 811,884,369 |
| VHDL | 648,401,444 |
| VisualBasic.NET | 1,005,680,881 |
| XML | 12,616,779,741 |
| YAML | 10,574,010,491 |
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:
| Task | N-Nano-V2 12B Base | N-Nano-V2 9B Base | Qwen3 8B Base | Gemma3 12B Base | |
|---|---|---|---|---|---|
| General | |||||
| MMLU | 78.24 | 74.53 | 76.44 | 73.61 | |
| MMLU-Pro 5-shot | 63.98 | 59.43 | 56.27 | 45.12 | |
| AGIEval English CoT | 68.03 | 65.28 | 59.54 | 51.69 | |
| Math | |||||
| GSM8K CoT | 91.66 | 91.36 | 84.00 | 74.45 | |
| Math | 83.54 | 80.50 | 55.40 | 42.40 | |
| MATH Level 5 | 67.61 | 63.64 | 29.91 | 17.71 | |
| AIME 2024 avg@32 | 56.67 | 30.00 | 20.00 | 16.67 | |
| Code | |||||
| HumanEval+ Pass@1 | 61.03 | 58.50 | 57.55 | 36.68 | |
| MBPP+ Pass@1 | 61.55 | 58.95 | 58.56 | 51.73 | |
| Commonsense Understanding | |||||
| ARC Challenge | 93.26 | 90.70 | 93.09 | 90.44 | |
| HellaSwag | 84.00 | 79.90 | 79.75 | 84.15 | |
| OpenBookQA | 46.00 | 44.80 | 42.00 | 46.00 | |
| PIQA | 82.54 | 81.83 | 79.43 | 82.10 | |
| WinoGrande | 79.24 | 75.30 | 75.93 | 79.95 | |
| Long Context | |||||
| RULER-128K | 84.74 | 82.22 | - | 80.70 |
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.
| Task | N-Nano-V2 12B Base | N-Nano-V2 9B Base | Qwen3 8B Base | Gemma3 12B Base | |
|---|---|---|---|---|---|
| Global-MMLU-Lite | |||||
| German | 74.50 | 68.25 | 75.50 | 69.75 | |
| Spanish | 76.50 | 72.75 | 75.00 | 74.00 | |
| French | 78.25 | 69.75 | 74.25 | 72.50 | |
| Italian | 76.50 | 73.25 | 72.75 | 74.00 | |
| Japanese | 71.00 | 67.00 | 70.00 | 71.50 | |
| Korean | 72.50 | 67.25 | 67.25 | 70.25 | |
| Portuguese | 76.25 | 71.25 | 72.50 | 75.75 | |
| Chinese | 75.50 | 69.25 | 75.25 | 67.25 | |
| Average | 75.13 | 69.94 | 72.81 | 71.88 | |
| Multilingual Math (MGSM) | |||||
| Spanish | 93.20 | 91.60 | 86.40 | 74.00 | |
| German | 89.60 | 89.60 | 78.80 | 68.80 | |
| French | 86.40 | 86.00 | 78.80 | 70.80 | |
| Chinese | 44.40 | 75.20 | 28.80 | 26.80 | |
| Japanese | 76.00 | 74.80 | 30.80 | 26.40 | |
| Russian | 90.40 | 91.60 | 83.60 | 76.00 | |
| Average | 80.00 | 84.80 | 64.53 | 57.13 |
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
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.
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