NVIDIA-Nemotron-Nano-VL

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
• S