---
pipeline_tag: text-generation
base_model:
- deepseek-ai/DeepSeek-V3.1
license: mit
library_name: Model Optimizer
tags:
- nvidia
- ModelOpt
- DeepSeekV3.1
- quantized
- NVFP4
- nvfp4
---

# Model Overview

## Description:
The NVIDIA DeepSeek-V3.1-FP4 model is the quantized version of the DeepSeek AI's DeepSeek-V3.1 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check [here](https://huggingface.co/deepseek-ai/DeepSeek-V3.1). The NVIDIA DeepSeek V3.1 FP4 model is quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).

This model is ready for commercial/non-commercial use.  <br>

## Third-Party Community Consideration
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [(DeepSeek V3.1) Model Card](https://huggingface.co/deepseek-ai/DeepSeek-V3.1).

### License/Terms of Use:
[MIT](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md)

### Deployment Geography:
Global <br>

### Use Case:
Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications. <br>

### Release Date:
Huggingface 11/25/2025 via https://huggingface.co/nvidia/DeepSeek-V3.1-NVFP4 <br>

## Model Architecture:
**Architecture Type:** Transformers  <br>
**Network Architecture:** DeepseekV3ForCausalLM <br>

## Input:
**Input Type(s):** Text <br>
**Input Format(s):** String <br>
**Input Parameters:** 1D (One Dimensional): Sequences <br>
**Other Properties Related to Input:** DeepSeek recommends adhering to the following configurations when utilizing the DeepSeek-V3.1 series models, including benchmarking, to achieve the expected performance: \

- Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
- Avoid adding a system prompt; all instructions should be contained within the user prompt.
- For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
- When evaluating model performance, it is recommended to conduct multiple tests and average the results. <br>

## Output:
**Output Type(s):** Text <br>
**Output Format:** String <br>
**Output Parameters:** 1D (One Dimensional): Sequences <br>
**Other Properties Related to Output:** N/A <br>

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. <br>

## Software Integration:
**Runtime Engine(s):** <br>
* TensorRT-LLM <br>

**Supported Hardware Microarchitecture Compatibility:** <br>
* NVIDIA Blackwell <br>

**Preferred Operating System(s):** <br>
* Linux <br>

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):
** The model is quantized with nvidia-modelopt **v0.39.0**  <br>

## Training Dataset: <br>
** Data Collection Method by dataset: Hybrid: Human, Automated <br>
** Labeling Method by dataset: Hybrid: Human, Automated <br>

## Testing Dataset: <br>
** Data Collection Method by dataset: Hybrid: Human, Automated <br>
** Labeling Method by dataset: Hybrid: Human, Automated <br>

## Evaluation Dataset: <br>
** Data Collection Method by dataset: Hybrid: Human, Automated <br>
** Labeling Method by dataset: Hybrid: Human, Automated <br>

## Calibration Datasets:
* Calibration Dataset: [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) <br>
** Data collection method: Automated. <br>
** Labeling method: Automated. <br>


## Inference:
**Acceleration Engine:** TensorRT-LLM <br>
**Test Hardware:** B200 <br>

## Post Training Quantization
This model was obtained by quantizing the weights and activations of DeepSeek V3.1 to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformer blocks are quantized. This optimization reduces the number of bits per parameter from 8 to 4, reducing the disk size and GPU memory requirements by approximately 1.6x.

## Usage

### Deploy with TensorRT-LLM

To deploy the quantized FP4 checkpoint with [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) LLM API, follow the sample codes below (you need 8xB200 GPU and TensorRT LLM built from source with the latest main branch):

* LLM API sample usage:
```
from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM

def main():

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(max_tokens=32)

    llm = LLM(model="nvidia/DeepSeek-V3.1-FP4", tensor_parallel_size=8, enable_attention_dp=True)

    outputs = llm.generate(prompts, sampling_params)

    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")


# The entry point of the program needs to be protected for spawning processes.
if __name__ == '__main__':
    main()

```

### Evaluation
The accuracy benchmark results are presented in the table below:
<table>
  <tr>
   <td><strong>Precision</strong>
   </td>
   <td><strong>MMLU-Pro</strong>
   </td>
   <td><strong>GPQA Diamond</strong>
   </td>
   <td><strong>LiveCodeBench</strong>
   </td>
   <td><strong>SCICODE</strong>
   </td>
   <td><strong>AIME 2025</strong>
   </td>
  </tr>
  <tr>
   <td>FP8 (AA Ref)
   </td>
   <td>85
   </td>
   <td>79
   </td>
   <td>78
   </td>
   <td>42
   </td>
   <td>85
   </td>
  </tr>
  <tr>
   <td>FP4
   </td>
   <td>83
   </td>
   <td>77
   </td>
   <td>79
   </td>
   <td>44
   </td>
   <td>85
   </td>
  </tr>
  <tr>
</table>

*Max OSL for evals can be as high as 64K.

## Model Limitations:
The base model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may 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.

## 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 model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).