NVIDIA-Nemotron-3-Super-120B-A12B-Base

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Model Overview

Model Developer: NVIDIA Corporation

Model Dates:

December 2025 - January 2026

Data Freshness:

• 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.

To get started, you can use our quickstart guide below.

License/Terms of Use

Use of these model weights is governed by the NVIDIA Nemotron Open Model License.

Base Benchmarks

Comparison of Ling-flash-base-2.0, GLM-4.5-Air-Base, and Nemotron Super 120B-A12B Base. Best results per row in bold.

All evaluation results were collected via Nemo Evaluator SDK and NVIDIA's open source container of LM Evaluation Harness, unless otherwise stated. For reproducibility purposes, more details on the evaluation settings can be found in the Nemo Evaluator SDK examples folder and the reproducibility tutorial for Nemotron 3 Super. The open source container on LM Evaluation Harness packaged via NVIDIA's Nemo Evaluator SDK used for evaluations can be found here

Deployment Geography: Global

Use Case

This model is intended for developers and researchers building LLMs.

Release Date

NGC - 03/04/2026 via Hugging Face - 03/04/2026 via

Reference(s)

• NVIDIA Nemotron 3 model family on Hugging Face
• NVIDIA Nemotron 3 Super Technical Report

Model Architecture

• 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.
• Software used for pre-training: Megatron-LM

NVIDIA-Nemotron-3-Super-120B-A12B-Base model is a result of the above work.

The end-to-end training recipe is available in the NVIDIA Nemotron Developer Repository. Evaluation results can be replicated using the NeMo Evaluator SDK. Data Designer is one of the libraries used to prepare the pre and post training datasets. More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron 3 Super Technical Report.

Input

• Input Type(s): Text
• Input Format(s): String
• Input Parameters: One-Dimensional (1D): Sequences
• 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.

Output

• Output Type(s): Text
• Output Format: String
• Output Parameters: One-Dimensional (1D): Sequences
• 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.

Software Integration

• Runtime Engine(s): NeMo 25.11.01
• Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere - A100; NVIDIA Blackwell; NVIDIA Hopper - H100-80GB
• 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 - 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.

Evaluation Dataset

• 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

@misc{nvidia_nemotron_3_2025,
 title = {NVIDIA Nemotron 3: Efficient and Open Intelligence},
 author = {{NVIDIA}},
 year = {2025},
 url = {https://arxiv.org/abs/2512.20856},
 note = {White Paper}
}

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