nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8

NVIDIA-Nemotron-Nano-VL-12B-V2-FP8

Model Overview

Description

NVIDIA-Nemotron-Nano-VL-12B-V2-FP8 is the quantized version of the NVIDIA Nemotron Nano VL V2 model, which is an auto-regressive vision language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Nemotron Nano VL FP4 QAD model is quantized with TensorRT Model Optimizer.

This model was trained on commercial images for all three stages of training and supports single image inference.

License/Terms of Use

Governing Terms:

Your use of the model is governed by the NVIDIA Open License Agreement.

Additional Information:

Backbone LLM: NVIDIA-Nemotron-Nano-12B-v2.

Deployment Geography:

Global

Use Case:

Customers: AI foundry enterprise customers

Use Cases: Image summarization. Text-image analysis, Optical Character Recognition, Interactive Q&A on images, Text Chain-of-Thought reasoning

Release Date:

• Build.Nvidia.com [October 28th, 2025] via nvidia/NVIDIA-Nemotron-Nano-VL-12B-V2
• Hugging Face [October 28th, 2025] via nvidia/NVIDIA-Nemotron-Nano-VL-12B-V2-BF16
• Hugging Face [October 28th, 2025] via nvidia/NVIDIA-Nemotron-Nano-VL-12B-V2-FP8
• Hugging Face [October 28th, 2025] via nvidia/NVIDIA-Nemotron-Nano-VL-12B-V2-NVFP4

Model Architecture:

Network Type: Transformer

Network Architecture:

Vision Encoder: C-RADIOv2-H

Language Encoder: NVIDIA-Nemotron-Nano-12B-v2

Input

Input Type(s): Image, Text

• Input Images
• Language Supported: German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese, English

Input Format(s): Image (Red, Green, Blue (RGB)), and Text (String)

Input Parameters: Image (2D), Text (1D)

Other Properties Related to Input:

• Context length up to 128K
• 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)

Output

Output Type(s): Text

Output Formats: String

Output Parameters: One-Dimensional (1D): Sequences up to 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
Supported Hardware Microarchitecture Compatibility: H100 SXM 80GB
Supported Operating System(s): Linux

Model Versions:

Nemotron-Nano-VL-12B-V2-FP8

Quick Start

Install Dependencies

pip install causal_conv1d "transformers>4.53,<4.54" torch timm "mamba-ssm==2.2.5" accelerate open_clip_torch numpy pillow

Usage

To serve this checkpoint with vLLM, you can start the docker vllm/vllm-openai:nightly and run the sample command below:

python3 -m vllm.entrypoints.openai.api_server --model nvidia/Nemotron-Nano-VL-12B-V2-FP8 --trust-remote-code --quantization modelopt

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

Type	Data Type	Total Samples	Total Size (GB)
Function call	text	8,000	0.02
Image Captioning	image, text	1,422,102	1,051.04
Image Reasoning	image, text	1,888,217	286.95
OCR	image, text	9,830,570	5,317.60
Referring Expression Grounding	image, text	14,694	2.39
Safety	image, text	34,187	9.21
Safety	text	57,223	0.52
Safety	video, text	12,988	11.78
Text Instruction Tuning	text	245,056	1.13
Text Reasoning	text	225,408	4.55
VQA	image, text	8,174,136	2,207.52
VQA	video, text	40,000	46.05
Video Captioning	video, text	3,289	6.31
Video Reasoning	video, text	42,620	49.10
VideoQA	video, text	1,371,923	17,641.79
Visual Instruction Tuning	image, text	1,173,877	167.79
TOTAL		24,544,290	26,803.75

Private Datasets

Type	Modalities	Total Samples	Total Size (GB)
Image Reasoning	image, text	17,729	15.41
Text Reasoning	text	445,958	9.01
TOTAL		463,687	24.42

Data Crawling and Scraping

Type	Modalities	Total Samples	Total Size (GB)
Image Captioning	image, text	39,870	10.24
VQA	image, text	40,348	3.94
VideoQA	video, text	288,728	393.30
TOTAL		368,946	407.48

User-Sourced Data (Collected by Provider including Prompts)

Self-Sourced Synthetic Data

Type	Data Type	Total Samples	Total Size (GB)
Code	text	1,165,591	54.15
OCR	image, text	216,332	83.53
Text Reasoning	text	12,727,857	295.80
TOTAL		14,109,780	433.48

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:

Dataset
AI2D Test
ChartQA Test
OCRBench
OCRBenchV2 English
DocVQA Val

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:

Benchmark	Score (FP8)	Score (BF16)
AI2D	87.6%	87.1%
OCRBenchV2	61.8%	62.0%
OCRBench	85.4%	85.6%
ChartQA	89.4%	89.7%
DocVQA val	94.3%	94.4%

Inference:

Engine: vLLM
Test Hardware:

• 1x NVIDIA H100 SXM 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 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++ Explainability, Bias, Safety & Security, and Privacy Subcards. 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.