The post-training data has a cutoff date of February 2026.
The pre-training data has a cutoff date of December 2025.
Description
Nemotron-3-Super-120B-A12B-Base is a base large language model (LLM) trained from scratch by NVIDIA, with next token prediction loss. It provides a good starting platform for further training, including instruction follow, and coding.
The model employs a hybrid Latent Mixture-of-Experts (LatentMoE) architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. Distinct from the Nano model, the Super model incorporates Multi-Token Prediction (MTP) layers for faster text generation and improved quality, and it is trained using NVFP4 quantization to maximize compute efficiency. The model has 12B active parameters and 120B parameters in total.
The supported languages include: English, Spanish, French, German, Japanese, Italian, Chinese, Arabic, Hebrew, Hindi, Korean, Czech, Danish, Dutch, Finnish, Polish, Portuguese, Thai, Swedish, and Russian.
This model is ready for commercial use.
What is Nemotron?
NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.
Architecture Type: Mamba2-Transformer Hybrid Latent Mixture of Experts (LatentMoE) with Multi-Token Prediction (MTP)
Network Architecture: Nemotron Hybrid LatentMoE
Number of model parameters: 120B Total / 12B Active
Model Design
The model utilizes the LatentMoE architecture, where tokens are projected into a smaller latent dimension for expert routing and computation, improving accuracy per byte. The Super model is trained using NVFP4 (weight, activation, and gradient tensors are quantized to NVFP4) to maximize throughput on supported hardware. The model includes Multi-Token Prediction (MTP) layers, which predict multiple future tokens to provide richer training signals and enable faster inference via speculative decoding.
Training Methodology
Stage 1: Pre-Training
NVIDIA-Nemotron-3-Super-120B-A12B-Base model was pre-trained for over 25T tokens using crawled and synthetic code, math, science, and general knowledge data. Training leveraged NVFP4 quantization for efficiency. All datasets are disclosed in the Training and Evaluation Datasets section of this document. Major portions of the pre-training corpus are released in the Nemotron-Pre-Training-Datasets collection.
Other Properties Related to Input: Maximum context length up to 1M tokens. Supported languages include: English, Spanish, French, German, Japanese, Italian, Chinese, Arabic, Hebrew, Hindi, Korean, Czech, Danish, Dutch, Finnish, Polish, Portuguese, Thai, Swedish, and Russian.
Other Properties Related to Output: Maximum context length up to 1M 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.
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 - Base
Training and Evaluation Datasets:
Training
Data Modality: Text
The total size: 15,087,602,908,990
Total number of datasets: 153
Dataset partition:Training [100%], testing [0%], validation [0%]Time period for training data collection: 2013 to February 24, 2026
Time period for testing data collection: 2013 to February 24, 2026
Time period for validation data collection: 2013 to February 24, 2026
Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
NVIDIA-Nemotron-3-Super-120B-A12B-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 19 other 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 25T tokens.
More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron 3 Super.
Click to explore the full dataset catalogue used for training
Base Pre-Training Corpus (Nemotron 3 Foundation)
The foundation of the model is trained on the Nemotron-3-Nano corpus, comprising the following collections:
Dataset Collection
Token Counts
Description
Nemotron-CC-v2 & v2.1
9.13T
A massive collection of English web data filtered from Common Crawl, including 2.5T+ tokens of new organic, translated, and synthetically rephrased content.
Nemotron-CC-Code-v1
427.9B
High-quality code tokens extracted from Common Crawl using the Lynx + LLM pipeline to preserve structure and equations.
Nemotron-Pretraining-Code-v1 & v2
1.09T
Curated GitHub code references with multi-stage filtering, deduplication, and large-scale synthetic code data.
Nemotron-CC-Math-v1
133.3B
High-quality math pre-training dataset preserving LaTeX formatting and mathematical structures.
Nemotron-Pretraining-Specialized-v1
336.4B
Synthetic datasets targeting specialized domains such as STEM reasoning and scientific coding.
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
English Common Crawl 1.1
Text
Not disclosed
10/2/2025
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
Private Non-publicly Accessible Datasets of Third Parties
Dataset
Model(s) used
Global Regulation
Unknown
TAUS Translation Memory
Unknown
Scale HLE
Unknown
HackerRank Coding
Unknown
RL data for Search
Gemini 3; GPT-5
Private Non-publicly Accessible Datasets by NVIDIA
Dataset
Model(s) used
Simple Minesweeper
-
Simple Sudoku
-
Multitool Typewriter Hard
-
Machine Translation of News Commentary and TAUS Translation Memory
Data Collection Method by dataset: Hybrid: Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
Inference
Acceleration Engine: PyTorch
Test Hardware:
NVIDIA Hopper:
8x H100
1x H200
NVIDIA Grace Blackwell
GB200
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 internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
We advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: Safety and Explainability Subcards.
For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, and Privacy Subcards.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
Citation
bibtex
1@misc{nvidia_nemotron_3_2025,
2 title = {NVIDIA Nemotron 3: Efficient and Open Intelligence},
3 author = {{NVIDIA}},
4 year = {2025},
5 url = {https://arxiv.org/abs/2512.20856},
6 note = {White Paper}
7}
