nvidia/Qwen3-30B-A3B-NVFP4

NVIDIA

The NVIDIA Qwen3-30B-A3B FP4 model is the quantized version of Alibaba's Qwen3-30B-A3B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check [here](https://huggingface.co/Qwen/Qwen3-30B-A3B). The NVIDIA Qwen3-30B-A3B 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> 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 [(Qwen3-30B-A3B) Model Card](https://huggingface.co/Qwen/Qwen3-30B-A3B). [Apache license 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) Global <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> Huggingface 08/22/2025 via https://huggingface.co/nvidia/Qwen3-30B-A3B-FP4 <br> **Architecture Type:** Transformers <br> **Network Architecture:** Qwen3-30B-A3B <br> **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 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> **Supported Runtime Engine(s):** <br> * TensorRT-LLM <br> **Supported Hardware Microarchitecture Compatibility:** <br> * NVIDIA Blackwell <br> **Preferred Operating System(s):** <br> * Linux <br> The model is quantized with nvidia-modelopt **v0.31.0** <br> ** Link: [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) <br> ** Data collection method: Automated. <br> ** Labeling method: Automated. <br> ** Data Collection Method by Dataset: Undisclosed <br> ** Labeling Method by Dataset: Undisclosed<br> ** Properties: Undisclosed ** Data Collection Method by Dataset: Undisclosed <br> ** Labeling Method by Dataset: Undisclosed <br> ** Properties: Undisclosed <br> * 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> **Engine:** TensorRT-LLM <br> **Test Hardware:** B200 <br> This model was obtained by quantizing the weights and activations of Qwen3-30B-A3B 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. To deploy the quantized checkpoint with [TensorRT-LLM](https://github.com/NVIDIA/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/Qwen3-30B-A3B-FP4") outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") if __name__ == '__main__': main() The accuracy benchmark results are presented in the table below: 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.