Model Overview

Description:

NVIDIA Nemotron Nano 12B v2 VL model enables multi-image reasoning and video understanding, along with strong document intelligence, visual Q&A and summarization capabilities.

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 NVIDIA Open Model License Agreement.

Deployment Geography:

Global

Use Case:

Nemotron Nano 12B V2 VL is a model for multi-modal document intelligence. It would be used by individuals or businesses that need to process documents such as invoices, receipts, and manuals. The model is capable of handling multiple images of documents, up to four images at a resolution of 1k x 2k each, along with a long text prompt. The expected use is for tasks like summarization and Visual Question Answering (VQA). The model is also expected to have a significant advantage in throughput.

Release Date:

HF [10/28/2025] via URL
Build.Nvidia.com [10/28/2025] via URL

System Prompt

Nemotron Nano 12B V2 VL supports reasoning for text, image and multi-images inputs.

Reasoning behavior is controlled via the system prompt. By default, reasoning is OFF.

For video inputs, reasoning is not supported.

• To enable reasoning (text and images only), include /think in the system prompt:

{"role": "system", "content": "/think"}

• To disable reasoning, include /no_think in the system prompt:

{"role": "system", "content": "/no_think"}

Model Architecture:

Architecture Type: Transformer
Network Architecture: Vision Encoder: CRadioV2-H Language Encoder: NVIDIA-Nemotron-Nano-12B-v2

Number of model parameters: 12.6B

Input:

Input Type(s): Image, Video, Text
Input Format: Image (png,jpg,jpeg,webp), Video (MP4, MOV, WEBM), Text (String)
Input Parameters: Image (2D),Video(3D), Text (1D)
Other Properties Related to Input:

• Input Images Supported: 5
• Language Supported: English only
  
• Input + Output Token: 128K
• Minimum Resolution: 32 × 32 pixels
• Maximum Resolution: Determined by a 12-tile layout constraint, with each tile being 512 × 512 pixels. This supports aspect ratios such as:
  • 4 × 3 layout: up to 2048 × 1536 pixels
  • 3 × 4 layout: up to 1536 × 2048 pixels
  • 2 × 6 layout: up to 1024 × 3072 pixels
  • 6 × 2 layout: up to 3072 × 1024 pixels
  • Other configurations allowed, provided total tiles ≤ 12
• Channel Count: 3 channels (RGB)
• Alpha Channel: Not supported (no transparency)
  
• Frames: 2 FPS with min of 8 frame and max of 128 frames

Output:

Output Type(s): Text
Output Format: String
Output Parameters: 1D
Other Properties Related to Output: Input + Output Token: 128K

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 Engine(s):

• vLLM
  
• TRT-LLM
  

Supported Hardware Microarchitecture Compatibility:

• NVIDIA L40S
  
• NVIDIA A100
  
• NVIDIA B200
  
• NVIDIA H100/H200
  
• NVIDIA RTX PRO 6000 Server Edition
  
• NVIDIA GH100
  
• NVIDIA GH200
  
• NVIDIA GB200
  

Preferred/Supported 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:

Training Datasets:

Data Modalities
Total Size: 39.486.703 samples
Total Number of Datasets: 270

Text-only datasets: 33
Text-and-image datasets: 176
Video-and-text datasets: 61
Total size: 27.7 TB

Data modalities: Text, Image, Video
Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

Dataset partition: Training [100%], Testing [0%], Validation [0%]
Time period for training data collection: 2023-2025
Time period for testing data collection: N/A
Time period for validation data collection: N/A

The post-training datasets consist of a mix of internal and public datasets designed for training vision language models across various tasks. It includes:

• Public datasets sourced from publicly available images and annotations, supporting tasks like classification, captioning, visual question answering, conversation modeling, document analysis and text/image reasoning.
• Internal text and image datasets built with public commercial images and internal labels, adapted for the same tasks as listed above.
• Synthetic image datasets generated programmatically for specific tasks like tabular data understanding and optical character recognition (OCR), for English, Chinese as well as other languages.
• Video datasets supporting video question answering and reasoning tasks from publicly available video sources, with either publicly available or internally generated annotations.
• Specialized datasets for safety alignment, function calling, and domain-specific tasks (e.g., science diagrams, financial question answering).
• NVIDIA-Sourced Synthetic Datasets for text reasoning.
• Private datasets for safety alignment or VQA on invoices.
• Crawled or scraped captioning, VQA, and video datasets.
• Some datasets were improved with Qwen2.5-72B-Instruct annotations

For around ~30% of our total training corpus and several of the domains listed above, we used commercially permissive models to perform:

• Language translation
• Re-labeling of annotations for text, image and video datasets
• Synthetic data generation
• Generating chain-of-thought (CoT) traces

Additional processing for several datasets included rule-based QA generation (e.g., with templates), expanding short answers into longer responses, as well as proper reformatting. More details can be found here.

** Image based datasets were all scanned against known CSAM to make sure no such content was included in training.

Public Datasets

Private Datasets

Data Crawling and Scraping

User-Sourced Data (Collected by Provider including Prompts)

Self-Sourced Synthetic Data

Properties

• Additionally, the dataset collection (for training and evaluation) consists of a mix of internal and public datasets designed for training and evaluation across various tasks. It includes:
  • Internal datasets built with public commercial images and internal labels, supporting tasks like conversation modeling and document analysis.
  • Public datasets sourced from publicly available images and annotations, adapted for tasks such as image captioning and visual question answering.
  • Synthetic datasets generated programmatically for specific tasks like tabular data understanding.
  • Specialized datasets for safety alignment, function calling, and domain-specific tasks (e.g., science diagrams, financial question answering).

Evaluation Datasets:

The following external benchmarks are used for evaluating the model:

Data Collection Method by dataset:

• Hybrid: Human, Automated
  

Labeling Method by dataset:

• Hybrid: Human, Automated
  

Properties (Quantity, Dataset Descriptions, Sensor(s)): N/A

Dataset License(s): N/A

Evaluation benchmarks scores:

Inference:

Acceleration Engine: [vLLM]
Acceleration Engine: [TRT-LLM]

Test Hardware:

• NVIDIA L40S
  
• NVIDIA A100
  
• NVIDIA B200
  
• NVIDIA H100/H200
  
• NVIDIA RTX PRO 6000 Server Edition
  
• NVIDIA GH200
  
• NVIDIA 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.
Please report security vulnerabilities or NVIDIA AI Concerns here.
Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
Outputs generated by these models may contain political content or other potentially misleading information, issues with content security and safety, or unwanted bias that is independent of our oversight.