Qwen3-235B-A22B-NVFP4

Model Overview

Description:

The NVIDIA Qwen3-235B-A22B FP4 model is the quantized version of Alibaba's Qwen3-235B-A22B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3-235B-A22B FP4 model is quantized with 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 (Qwen3-235B-A22B) Model Card.

License/Terms of Use:

Apache license 2.0

Deployment Geography:

Global <br>

Use Case: <br>

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: <br>

Huggingface 08/22/2025 via https://huggingface.co/nvidia/Qwen3-235B-A22B-FP4 <br>

Model Architecture:

Architecture Type: Transformers <br> Network Architecture: Qwen3-235B-A22B <br>

Input:

Input Type(s): Text <br> Input Format(s): String <br> Input Parameters: 1D (One-Dimensional): Sequences <br> Other Properties Related to Input: Context length up to 131K <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:

Supported Runtime Engine(s): <br>

• TensorRT-LLM <br>

Supported Hardware Microarchitecture Compatibility: <br>

• NVIDIA Blackwell <br>

Preferred Operating System(s): <br>

• Linux <br>

Model Version(s):

The model is quantized with nvidia-modelopt v0.31.0 <br>

Training, Testing, and Evaluation Datasets:

Calibration Dataset:

** Link: cnn_dailymail <br> ** Data collection method: Automated. <br> ** Labeling method: Automated. <br>

Training Datasets:

** Data Collection Method by Dataset: Undisclosed <br> ** Labeling Method by Dataset: Undisclosed<br> ** Properties: Undisclosed

Testing Dataset:

** Data Collection Method by Dataset: Undisclosed <br> ** Labeling Method by Dataset: Undisclosed <br> ** Properties: Undisclosed <br>

Evaluation Dataset:

• Datasets: MMLU Pro, GPQA Diamond, HLE, LiveCodeBench, SciCode, HumanEval, AIME 2024, MATH-500 <br> ** Data collection method: Hybrid: Automated, Human <br> ** Labeling method: Hybrid: Human, Automated <br>

Inference:

Engine: TensorRT-LLM <br> Test Hardware: B200 <br>

Post Training Quantization

This model was obtained by quantizing the weights and activations of Qwen3-235B-A22B 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 16 to 4, reducing the disk size and GPU memory requirements by approximately 3.3x.

Usage

Deploy with TensorRT-LLM

To deploy the quantized checkpoint with TensorRT-LLM LLM API, follow the sample codes below:

• LLM API sample usage:

from tensorrt_llm import LLM, SamplingParams


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(temperature=0.8, top_p=0.95)

    llm = LLM(model="nvidia/Qwen3-235B-A22B-FP4", tensor_parallel_size=4)

    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>HLE</strong> </td> <td><strong>LiveCodeBench</strong> </td> <td><strong>MATH-500</strong> </td> <td><strong>AIME 2024</strong> </td> </tr> <tr> <td>BF16 (AA Ref) </td> <td>0.83 </td> <td>0.70 </td> <td>0.12 </td> <td>0.62 </td> <td>0.93 </td> <td>0.84 </td> </tr> <tr> <td>FP4 </td> <td>0.82 </td> <td>0.68 </td> <td>0.09 </td> <td>0.70 </td> <td>0.97 </td> <td>0.82 </td> </tr> <tr> </table>

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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