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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",[147,81,144,141],"imageUrl","/api/image/nvidia/aerial/containers/aerial-cuda-accelerated-ran","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",16351847931,"licenseTerms",{"_246":65,"_247":14,"_248":249,"_250":18},"isMultinodeEnabled","isMultiArchitecture","hasSignedTag","featureLabels",[],"displayLabels",[199,200,201,202,203,204,205,206,207,208,209],"policyLabels",[],"systemLabels",[191],"products",[],"hasNimMetadata",{"_161":192,"_193":192,"_194":195,"_196":197},"signed 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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