Model Overview

Description:

The NVIDIA DeepSeek V3-0324 FP4 model is the quantized version of DeepSeek AI's DeepSeek V3-0324 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA DeepSeek V3-0324 FP4 model is quantized with TensorRT Model Optimizer.

This model is ready for commercial/non-commercial use.

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 V3-0324) Model Card.

License/Terms of Use:

MIT

Deployment Geography:

Global

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.

Release Date:

Huggingface 06/06/2025 via https://huggingface.co/nvidia/DeepSeek-R1-0528-FP4

Model Architecture:

Architecture Type: Transformers Network Architecture: DeepSeek V3-0324

Input:

Input Type(s): Text Input Format(s): String Input Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Input: Context length up to 128K

Output:

Output Type(s): Text Output Format: String Output Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Output: N/A

Software Integration:

Supported Runtime Engine(s):

• TensorRT-LLM

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Blackwell

Preferred Operating System(s):

• Linux

Model Version(s):

The model is quantized with nvidia-modelopt v0.27.0

Training, Testing, and Evaluation Datasets:

Data Collection Method by Dataset: Undisclosed Labeling Method by Dataset: Undisclosed Properties: Undisclosed

Calibration Dataset:

** Link: cnn_dailymail ** Data collection method: Automated. ** Labeling method: Undisclosed.

• Evaluation Dataset: MMLU ** Data collection method: Undisclosed. ** Labeling method: N/A.

Inference:

Engine: TensorRT-LLM Test Hardware: B200

Post Training Quantization

This model was obtained by quantizing the weights and activations of DeepSeek V3-0324 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 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-V3-0324-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 need to be protected for spawning processes.
if __name__ == '__main__':
    main()

Benchmarks

This section compares the accuracy of the original DeepSeek V3-0324 model with our FP4-quantized version across benchmarks.

1 Reference scores for DeepSeek V3-0324 sourced from artificialanalysis.

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.

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