---
title: "llama-3.1-nemotron-nano-vl-8b-v1"
publisher: "nvidia"
type: "endpoint"
updated: "2025-07-01T18:33:30.461Z"
description: "Multi-modal vision-language model that understands text/img and creates informative responses"
canonical: "https://build.nvidia.com/nvidia/llama-3.1-nemotron-nano-vl-8b-v1"
---

# Model Overview

## Description:
Llama Nemotron Nano VL is a leading document intelligence vision language model (VLMs) that enables the ability to query and summarize images from the physical or virtual world. Llama Nemotron Nano VL is deployable in the data center, cloud and at the edge. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance.  

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

### License/Terms of Use
**Governing Terms:**  

Governing Terms: Your use of the service is governed by the [NVIDIA API Catalog Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Your use of the model is governed by the [NVIDIA Open License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). 

The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Your use of this model is governed by the NVIDIA Community Model License. 

**Additional Information:**  
[Llama 3.1 Community Model License](https://www.llama.com/llama3_1/license/); Built with Llama.

### Deployment Geography:
Global<br>

### Use Case: <br>
Customers: AI foundry enterprise customers<br>
Use Cases: Image summarization. Text-image analysis, Optical Character Recognition, Interactive Q&A on images.
<br>. 

## Release Date:  

Build.Nvidia.com [May 28th] via [URL] 
Hugging Face [May 28th]

## Model Architecture:
**Network Type:**
Transformer 
**Network Architecture:** 
Vision Encoder: CRadioV2-H
Language Encoder: LLAMA-3.1-8B-Instruct

### Input

Input Type(s): Image, Text
- Input Images
- Language Supported: English only

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

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

Other Properties Related to Input:

- Input + Output Token: 16K
- 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)

### Output
Output Type(s): Text

Output Formats: String

Output Parameters: 1D

Other Properties Related to Output: Input + Output Token: 16K

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): TensorRT-LLM<br>
Supported Hardware Microarchitecture Compatibility: H100 SXM 80GB<br>
Supported Operating System(s): Linux<br>

### Model Versions:
Llama-3.1-Nemotron-Nano-VL-8B-V1 

## Training/Evaluation Dataset:
NV-Pretraining and NV-CosmosNemotron-SFT were used for training and evaluation

Data Collection Method by dataset (Training and Evaluation):  <br>
* Hybrid: Human, Synthetic <br>

Labeling Method by dataset (Training and Evaluation):  <br>
* Hybrid: Human, Synthetic <br>

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: <br>
• Internal datasets built with public commercial images and internal labels, supporting tasks like conversation modeling and document analysis.<br>
• Public datasets sourced from publicly available images and annotations, adapted for tasks such as image captioning and visual question answering.<br>
• Synthetic datasets generated programmatically for specific tasks like tabular data understanding.<br>
• Specialized datasets for safety alignment, function calling, and domain-specific tasks (e.g., science diagrams, financial question answering).<br>

## Evaluation Benchmarks:

| Benchmark | Score |
| --- | --- |
| MMMU Val with chatGPT as a judge | 48.2% |
| AI2D | 84.8% |
| ChartQA  | 86.3% |
| InfoVQA Val | 76.2% |
| OCRBench | 839 |
| OCRBenchV2 English | 60.1% |
| OCRBenchV2 Chinese | 37.9% |
| DocVQA val | 91.2% |
| VideoMME  | 49.2% |

# Inference:
**Engine:** TTensorRT-LLM <br>
**Test Hardware:** <br>
* 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 [Insert Link to Model Card++ here].  Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

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.

## Bias

Field                                                                                               | Response
:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Participation considerations from adversely impacted groups [protected classes](https://calcivilrights.ca.gov/disputeresolution/protected-characteristics/) in model design and testing:  | We actively considered participation from adversely impacted groups and protected classes during model design and testing by engaging diverse stakeholders, reviewing data for representation, and evaluating outputs for bias. Feedback channels were provided throughout development.
Measures taken to mitigate against unwanted bias:                                                   | We took several steps to reduce unwanted bias, including:<br>- **Evaluating** the model’s answers with regard to fairness for different groups<br>- Using tools to **identify** and measure unfairness.

## Explainability

Field                                                                                                  | Response
:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
Intended Application & Domain:                                                                         | Visual Question Answering
Model Type:                                                                                            | Transformer
Intended Users:                                                                                        | Generative AI creators working with conversational AI models and image content.
Output:                                                                                                | Text (Responds to posed question, stateful - remembers previous answers)
Describe how the model works:                                                                          | Chat based on image/text
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:   | Not Applicable
Technical Limitations:                                                                                 | <br>**Context Length:** Supports up to 16,000 tokens total (input + output). If exceeded, input is truncated from the start, and generation ends with an EOS token. Longer prompts may risk performance loss.<br><br>If the model fails (e.g., generates incorrect responses, repeats, or gives poor responses), issues are diagnosed via benchmarks, human review, and internal debugging tools. Only use NVIDIA provided models that use safetensors format. <br><br>Do not expose the vLLM host to a network where any untrusted connections may reach the host. Only use NVIDIA provided models that use safetensors format.
Verified to have met prescribed NVIDIA quality standards:                                              | Yes
Performance Metrics:                                                                                   | MMMU Val with chatGPT as a judge, AI2D, ChartQA Test, InfoVQA Val, OCRBench, OCRBenchV2 English, OCRBenchV2 Chinese, DocVQA val, VideoMME (16 frames), SlideQA (F1)
Potential Known Risks:                                                                                 | The Model may produce output that is biased, toxic, or incorrect responses. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The Model may also generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text, producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.<br>While we have taken safety and security into account and are continuously improving, outputs may still contain political content, misleading information, or unwanted bias beyond our control.
Licensing:                                                                                             | **Governing Terms:**<br>Your use of the software container and model is governed by the [NVIDIA Software and Model Evaluation License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/).<br><br>**Additional Information:**<br>[Llama 3.1 Community Model License](https://www.llama.com/llama3_1/license/); Built with Llama.

## Privacy

Field                                                                                                                              |  Response
:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
Generatable or reverse engineerable personal data?                                                     |  None
Personal data used to create this model?                                                                                       |  None
How often is dataset reviewed?                                                                                                     |  Before Every Release                                                  |  Yes
Does data labeling (annotation, metadata) comply with privacy laws?                                                                |  Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made?                           |  No, not possible with externally-sourced data. Applicable Privacy Policy: https://www.nvidia.com/en-us/about-nvidia/privacy-policy/

## Safety & Security

Field                                               |  Response
:---------------------------------------------------|:----------------------------------
Model Application(s):                               | - Extracting and understanding information from text and images in documents (OCR, tables, charts, diagrams, math expressions)<br>- Recognizing objects, attributes, and semantic relationships in images<br>- Interactive Q&A based on images and text<br>- Analyzing and summarizing similarities and differences between images
Describe the life critical impact (if present).     |  Not Applicable
Use Case Restrictions:                              | Governing Terms: Your use of the service is governed by the [NVIDIA API Catalog Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Your use of this model is governed by the NVIDIA Community Model License.  Additional Information: [Llama 3.1 Community Model License](https://www.llama.com/llama3_1/license/); Built with Llama.
Model and dataset restrictions:                     |  The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.

## Prototype

```python
import requests

invoke_url = "https://integrate.api.nvidia.com/v1/chat/completions"

headers = {
"Authorization": "Bearer ",
"Accept": "application/json",
}

payload = {
"messages": [
{
"role": "user",
"content": ""
}
]
}

# re-use connections
session = requests.Session()

response = session.post(invoke_url, headers=headers, json=payload)

response.raise_for_status()
response_body = response.json()
print(response_body)
```

```javascript
import fetch from "node-fetch";

const invokeUrl = "https://integrate.api.nvidia.com/v1/chat/completions"

const headers = {
"Authorization": "Bearer ",
"Accept": "application/json",
}

const payload = {
"messages": [
{
"role": "user",
"content": ""
}
]
}

let response = await fetch(invokeUrl, {
method: "post",
body: JSON.stringify(payload),
headers: { "Content-Type": "application/json", ...headers }
});

let response_body = await response.json()

console.log(JSON.stringify(response_body))
```

```bash
invoke_url='https://integrate.api.nvidia.com/v1/chat/completions'

authorization_header='Authorization: Bearer '
accept_header='Accept: application/json'
content_type_header='Content-Type: application/json'

data=$'{
"messages": [
{
"role": "user",
"content": ""
}
]
}'

response=$(curl --silent -i -w "\n%{http_code}" --request POST \
--url "$invoke_url" \
--header "$authorization_header" \
--header "$accept_header" \
--header "$content_type_header" \
--data "$data"
)

echo "$response"
```