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Governing Terms: Use of this software is governed by the NVIDIA Software License Agreementand Product-Specific Terms for NVIDIA AI Products.

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NVIDIA Aerial™ CUDA-Accelerated RAN

Overview

NVIDIA Aerial™ CUDA-Accelerated RAN is an SDK (Software Development Kit)\nfor building commercial-grade, AI-native, 3GPP, and O-RAN compliant\n5G/6G gNB software on NVIDIA-accelerated computing platforms.

The SDK in this container includes:

\n
GPU-Accelerated 5G PHY (cuPHY): CUDA-based physical layer processing for 5G NR including channel coding (LDPC, Polar), modulation/demodulation, MIMO processing, and channel estimation
\n
\n
GPU-Accelerated MAC Scheduler (cuMAC): High-performance L2\nscheduler acceleration for resource allocation and scheduling
\n
\n
Python API (pyAerial): Python bindings for AI/ML research and\nintegration with frameworks like TensorFlow and Sionna
\n
\n
5G Reference Models (5GModel): MATLAB-based 5G waveform generation\nand test vector creation based on 3GPP specifications
\n

NVIDIA Aerial™ CUDA-Accelerated RAN is a part of NVIDIA AI Aerial™,\na portfolio of accelerated computing platforms, software and tools to\nbuild, train, simulate, and deploy AI-native wireless networks.

Getting Started

The SDK has components on GitHub and NGC.

Prerequisites

The following software components are required on the host:

CUDA 13.1.1 driver (590.48.01)
GDRCopy 2.5.1 (Not required for DGX Spark)
Nvidia container toolkit:\n
- https://github.com/NVIDIA/nvidia-docker
\n
Docker:\n
- https://docs.docker.com/install/linux/docker-ce/ubuntu/
\n
Supported CPU, GPU and NIC combinations:\n
- GH200: Grace Hopper MGX + BF3
- DGX Spark: GB10 + CX7
\n

Using Pre-Built Container (Recommended)

Clone repository

git clone https://github.com/NVIDIA/aerial-cuda-accelerated-ran.git --recurse-submodules\ncd aerial-cuda-accelerated-ran\n

Enable git LFS (if needed for large files)

git lfs install\ngit lfs pull\n

Pull the Aerial container from NGC

docker pull nvcr.io/nvidia/aerial/aerial-cuda-accelerated-ran:26-1-cubb\n

Start interactive development container

./cuPHY-CP/container/run_aerial.sh\n

Inside container: Build SDK

./testBenches/phase4_test_scripts/build_aerial_sdk.sh\n

Further Information

Visit the full documentation at NVIDIA Docs Hub

Support

File issues on GitHub for bugs and feature requests
Join discussions for questions and community support

Acknowledgements

See Acknowledgements

Citation

If you use NVIDIA Aerial™ CUDA-Accelerated RAN in your research, please cite:

@software{nvidia_aerial_cuda_accelerated_ran,\n  title = {NVIDIA Aerial™ CUDA-Accelerated RAN},\n  author = {NVIDIA Corporation},\n  year = {2025-2026},\n  url = {https://github.com/NVIDIA/aerial-cuda-accelerated-ran}\n}\n

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scheduling\n\n- **Python API (pyAerial)**: Python bindings for AI/ML research and\n integration with frameworks like TensorFlow and Sionna\n\n- **5G Reference Models (5GModel)**: MATLAB-based 5G waveform generation\n and test vector creation based on 3GPP specifications\n\nNVIDIA Aerial™ CUDA-Accelerated RAN is a part of [NVIDIA AI Aerial™](https://developer.nvidia.com/industries/telecommunications/ai-aerial),\na portfolio of accelerated computing platforms, software and tools to\nbuild, train, simulate, and deploy AI-native wireless networks.\n\n## Getting Started\n\nThe SDK has components on GitHub and NGC.\n\n### Prerequisites\n\nThe following software components are required on the host:\n\n- CUDA 13.1.1 driver (590.48.01)\n- GDRCopy 2.5.1 (Not required for DGX Spark)\n- Nvidia container toolkit:\n - \n- Docker:\n - \n- Supported CPU, GPU and NIC combinations:\n - GH200: Grace Hopper MGX + BF3\n - DGX Spark: GB10 + CX7\n\n### Using Pre-Built Container (Recommended)\n\n#### Clone repository\n\n```bash\ngit clone https://github.com/NVIDIA/aerial-cuda-accelerated-ran.git --recurse-submodules\ncd aerial-cuda-accelerated-ran\n```\n\n#### Enable git LFS (if needed for large files)\n\n```bash\ngit lfs install\ngit lfs pull\n```\n\n#### Pull the Aerial container from NGC\n\n```bash\ndocker pull nvcr.io/nvidia/aerial/aerial-cuda-accelerated-ran:26-1-cubb\n```\n\n#### Start interactive development container\n\n```bash\n./cuPHY-CP/container/run_aerial.sh\n```\n\n#### Inside container: Build SDK\n\n```bash\n./testBenches/phase4_test_scripts/build_aerial_sdk.sh\n```\n\n## Further Information\n\nVisit the full documentation at [NVIDIA Docs Hub](https://docs.nvidia.com/aerial/)\n\n## Support\n\n- File issues on GitHub for bugs and feature requests\n- Join discussions for questions and community support\n\n## Acknowledgements\n\nSee [Acknowledgements](https://docs.nvidia.com/aerial/cuda-accelerated-ran/latest/release_notes/acknowledgements.html)\n\n## Citation\n\nIf you use NVIDIA Aerial™ CUDA-Accelerated RAN in your research, please cite:\n\n```bibtex\n@software{nvidia_aerial_cuda_accelerated_ran,\n title = {NVIDIA Aerial™ CUDA-Accelerated RAN},\n author = {NVIDIA Corporation},\n year = {2025-2026},\n url = {https://github.com/NVIDIA/aerial-cuda-accelerated-ran}\n}\n```","displayName","Aerial CUDA-Accelerated RAN",["D",1776200046248],"publisher","NVIDIA","latestVersion","versions",[109,146,144,141],"imageUrl","/api/image/nvidia/aerial/containers/aerial-cuda-accelerated-ran","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",16351847931,"licenseTerms",{"_245":63,"_246":14,"_247":248,"_249":18},"isMultinodeEnabled","isMultiArchitecture","hasSignedTag","featureLabels",[],"displayLabels",[198,199,200,201,202,203,204,205,206,207,208],"policyLabels",[],"systemLabels",[190],"products",[],"hasNimMetadata",{"_160":191,"_192":191,"_193":194,"_195":196},"signed 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