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.
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.
** 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:
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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.
