1. [Topics](/topics)

[Telecommunication](/topics/telecommunications)

AI Aerial

#### Aerial Developer Forum

Have questions about NVIDIA AI Aerial or tips and tricks to share? Join the community on the Aerial Developer Forum. Click the link above to access.

[Access](https://forums.developer.nvidia.com/c/accelerated-computing/aerial/674)

# NVIDIA AI Aerial

NVIDIA AI Aerial™ is a suite of accelerated computing platforms, software libraries, and tools to build, train, simulate, and deploy AI-native wireless networks. It enables developers and researchers to go from rapid prototyping to commercial development of AI-RAN solutions for 5G and 6G that telcos can deploy.

[Get the Code](https://github.com/NVIDIA/aerial-cuda-accelerated-ran "Get the Code")[Read Documentation](https://docs.nvidia.com/aerial/index.html "Read Documentation")[Join 6G Developer Program  
  
  
](/6g-program "Join 6G Developer Program")

* * *

## NVIDIA AI Aerial Software

Explore open source software libraries and tools for building, training, simulating, and deploying AI-native wireless networks. Access to NVIDIA Aerial software, including AODT Client, is available [on GitHub](https://github.com/NVIDIA/aerial-cuda-accelerated-ran). For complete AODT software, access is available through the [NVIDIA 6G Developer Program](/6g-program).

### NVIDIA Sionna

A GPU-accelerated, differentiable, open source library for 5G and 6G communications research. It features a lightning-fast ray tracer for radio propagation, a link-level simulator, and system-level simulation capabilities.

[Go to Sionna GitHub](https://github.com/NVlabs/sionna)

[Read Sionna Documentation](https://nvlabs.github.io/sionna/)

### NVIDIA Aerial Framework

A toolchain for generating high-performance, GPU-accelerated 5G/6G pipelines from Python and a modular, real-time runtime for executing the pipelines on NVIDIA Aerial™ RAN Computer platforms.

[Go to Aerial Framework GitHub](https://github.com/NVIDIA/aerial-framework)

[Read Aerial Framework Documentation](https://docs.nvidia.com/aerial/framework/latest/)

### NVIDIA Aerial CUDA-Accelerated RAN

An SDK (Software Development Kit) for building commercial-grade, AI-native, 3GPP, and O-RAN compliant 5G/6G gNB software on NVIDIA-accelerated computing platforms  
Go to ACAR GitHub

[Go to ACAR GitHub](https://github.com/NVIDIA/aerial-cuda-accelerated-ran)

[Read Aerial CUDA-Accelerated RAN Documentation](https://docs.nvidia.com/aerial/cuda-accelerated-ran/latest/index.html)

### NVIDIA Aerial Omniverse Digital Twin (AODT)

A platform for creating next-generation network digital twins, enabling physically accurate virtual 5G and 6G wireless networks, from single towers to full cities.

[Go to AODT Client GitHub](https://github.com/NVIDIA/aerial-omniverse-digital-twin)

[Access AODT Complete (Membership Required)](https://developer.nvidia.com/6g-program)

[Read Aerial Omniverse Digital Twin Documentation](https://docs.nvidia.com/aerial/aodt/welcome)

* * *

## Hardware Platforms

#### NVIDIA AI Aerial Research Platforms

These systems enable the building, training, and over-the-air testing of AI-native wireless innovations for 5G and 6G.

 ![NVIDIA Sionna Research Kit](https://developer.download.nvidia.com/images/aerial/sionna-research-kit-ari.jpg)
#### Sionna Research Kit

Built on an open source foundation and powered by NVIDIA’s state-of-the-art, GPU-accelerated libraries, the Sionna Research Kit makes rapid prototyping, training, and deployment of cutting-edge 5G and 6G algorithms achievable for everyone—from seasoned professionals to students. 

[Learn More About Sionna Research Kit](https://nvlabs.github.io/sionna/rk/index.html)

 ![NVIDIA Aerial Testbed (ARC-OTA)](https://developer.download.nvidia.com/images/aerial/aerial-testbed-ari.jpg)
#### Aerial Testbed (ARC-OTA)

An end-to-end system that includes Aerial CUDA-Accelerated RAN combined with open source software (OAI L2+ and 5G Core) running on NVIDIA GH200 or DGX Spark™, over the air. Used for product development and performance optimization of commercial-grade and software-defined AI-RAN solutions.

[Learn More About Aerial Testbed  
](https://docs.nvidia.com/aerial/aerial-ran-colab-ota/current/index.html)

#### NVIDIA AI Aerial Deployment Platforms

The Aerial RAN Computer (ARC) family delivers high-performance, scalable, and accelerated computing platforms for telecom networks, enabling commercial AI-RAN deployments.

 ![NVIDIA Aerial RAN Computer-1](https://developer.download.nvidia.com/images/aerial/aerial-ran-computer-1.jpg)
#### Aerial RAN Computer-1

A modular and high-performance AI-RAN platform for high-density deployments, suited for AI-centric workloads and designed to scale from distributed RAN (D-RAN) to centralized RAN (C-RAN) at mobile switching offices. 

[Learn More About Aerial RAN Computer-1](https://developer.nvidia.com/blog/bringing-ai-ran-to-a-telco-near-you/)

 ![NVIDIA ARC-Compact](https://developer.download.nvidia.com/images/aerial/arc-compact.jpg)
#### ARC-Compact

An energy-efficient and high-performance AI-RAN platform for cell sites, suited for RAN-centric workloads and designed to meet the form-factor and environmental requirements for distributed RAN deployments.

[Learn More About ARC-Compact](https://developer.nvidia.com/blog/deploy-ai-ran-at-cell-sites-with-nvidia-arc-compact/)

 ![NVIDIA ARC-Pro](https://developer.download.nvidia.com/images/aerial/arc-pro(1).jpg)
#### ARC-Pro

A high-performance, energy-efficient AI-RAN platform featuring NVIDIA Blackwell RTX PRO™ GPUs, designed for on-ramping to AI-native 5G and 6G with advanced computing, connectivity, and sensing. Telco-optimized form factor, suitable for upgrading existing sites or greenfield deployments.

[Learn More About ARC-Pro](https://resources.nvidia.com/en-us-aerial-ran-computer-pro)

* * *

## Get Started With NVIDIA AI Aerial 

### Build and Train

Build and train AI/ML models for RAN, physical (PHY), and media access control (MAC) layers.

- 
[Explore Sionna Documentation](https://nvlabs.github.io/sionna/)
- 
[Access pyAerial Docs](https://docs.nvidia.com/aerial/archive/cuda-accelerated-ran/24-1/pyaerial/index.html)
- 
[Learn About Aerial Data Lake](https://docs.nvidia.com/aerial/archive/cuda-accelerated-ran/24-1/aerial_data_lake/index.html)
- 
[Begin With Aerial CUDA-Accelerated RAN](https://docs.nvidia.com/aerial/cuda-accelerated-ran/latest/index.html)
- 

[Get Started With Aerial Framework](https://docs.nvidia.com/aerial/framework/latest/)

### Simulate

Run large-scale, photorealistic 5G and 6G wireless network scenarios

- 
[Learn About Sionna SYS](https://nvlabs.github.io/sionna/sys/index.html)
- 
[Use Sionna Ray-Tracing Guide](https://github.com/NVlabs/sionna-rt)
- 
[Use Aerial Data Lake for Simulation Data](https://docs.nvidia.com/aerial/cuda-accelerated-ran/latest/data_lake/index.html)
- 
[Learn About Aerial Omniverse Digital Twin](https://docs.nvidia.com/aerial/aodt/welcome)
- 

[Get Started With Aerial Omniverse Digital Twin](https://docs.nvidia.com/aerial/aodt/installation)

### Deploy

Implement and validate in live and edge networks at scale.

- 
[Order Sionna Research Kit](https://nvlabs.github.io/sionna/rk/quickstart.html)
- 
[Refer to Aerial Testbed Documentation](https://docs.nvidia.com/aerial/aerial-ran-colab-ota/current/index.html)
- 
[Read About Aerial RAN Computer-1](https://developer.nvidia.com/blog/bringing-ai-ran-to-a-telco-near-you/)
- 

[Learn About ARC-Compact](https://developer.nvidia.com/blog/deploy-ai-ran-at-cell-sites-with-nvidia-arc-compact/)

- 

[Explore ARC-Pro](https://resources.nvidia.com/en-us-aerial-ran-computer-pro)

* * *

## Learning Library



The rows below are the raw result records for this component — one row per record. Column names are the source index fields; the full data is also downloadable in the links that follow.

| title | featured | x_formats | document_url | technologies | document_date | short_summary | document_title | learning_level | x_content_types |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Improve AI-Native 6G Design with the NVIDIA Aerial Omniverse Digital Twin | false | blog | https://developer.nvidia.com/blog/improve-ai-native-6g-design-with-the-nvidia-aerial-omniverse-digital-twin/ | Aerial Omniverse Digital Twin | 2025-12-09T05:00:00.000Z | Explore how AODT improves AI-native 6G network design with high-fidelity radio simulation. | Improve AI-Native 6G Design with the NVIDIA Aerial Omniverse Digital Twin | Technical - Intermediate | Explainer |
| Introducing NVIDIA Aerial Research Cloud for Innovations in 5G and 6G | false | blog | https://developer.nvidia.com/blog/introducing-aerial-research-cloud-for-innovations-in-5g-and-6g/ | AI Aerial | 2023-03-23T04:00:00.000Z | Get introduced to Aerial Research Cloud, NVIDIA's programmable 5G/6G research sandbox. | Introducing NVIDIA Aerial Research Cloud for Innovations in 5G and 6G | Technical - Beginner | Overview |
| Deploy AI-RAN at Cell Sites with NVIDIA ARC-Compact | false | blog | https://developer.nvidia.com/blog/deploy-ai-ran-at-cell-sites-with-nvidia-arc-compact/ | AI Aerial | 2025-05-18T04:00:00.000Z | Deploy AI-RAN at distributed cell sites using the NVIDIA ARC-Compact reference architecture. | Deploy AI-RAN at Cell Sites with NVIDIA ARC-Compact | Technical - Intermediate | How-to |
| Hello Sionna! A Demo of the First Open Source Library for Physical Layer Research | false | video | https://www.youtube.com/watch?v=cYUNE4i4Q4E | Sionna | 2022-03-22T04:00:00.000Z | Watch a quickstart demo of the Sionna open-source library for physical-layer research. | Hello Sionna! A Demo of the First Open Source Library for Physical Layer Research | Technical - Beginner | Demo |
| NVIDIA Aerial CUDA-Accelerated RAN | false | webpage | https://developer.nvidia.com/aerial-cuda-accelerated-ran | Aerial CUDA Accelerated RAN | 2024-04-22T04:00:00.000Z | Get introduced to Aerial CUDA-Accelerated RAN, NVIDIA's software-defined 5G/6G physical layer. | NVIDIA Aerial CUDA-Accelerated RAN | Technical - Beginner | Overview |
| aerial-sample-apps (GitHub) | true | code | https://github.com/NVIDIA/aerial-sample-apps | Aerial CUDA Accelerated RAN | 2026-04-29T04:00:00.000Z | Study distributed apps (dApps) using the E3 interface for real-time RAN data processing on Aerial Testbed. | aerial-sample-apps (GitHub) | Technical - Intermediate | Samples |
| Sionna — An Open-Source Library for 6G Research | false | webpage | https://developer.nvidia.com/sionna | Sionna | 2022-03-22T04:00:00.000Z | Get introduced to Sionna, NVIDIA's open-source library for physical-layer 6G research. | Sionna — An Open-Source Library for 6G Research | Technical - Beginner | Overview |
| NVIDIA 6G Research Cloud Platform | false | video | https://www.youtube.com/watch?v=WBK5pmQDquw | AI Aerial | 2024-03-25T04:00:00.000Z | Watch an overview of the NVIDIA 6G Research Cloud platform for accelerated wireless research. | NVIDIA 6G Research Cloud Platform | Technical - Beginner | Overview |
| aerial-framework (GitHub) | false | code | https://github.com/NVIDIA/aerial-framework | Aerial Framework | 2025-12-10T05:00:00.000Z | Fork the Aerial Framework toolchain for generating GPU-accelerated 5G/6G pipelines from Python. | aerial-framework (GitHub) | Technical - Beginner | Samples |
| Sionna Documentation | false | webpage | https://nvlabs.github.io/sionna/ | Sionna | 2022-03-22T04:00:00.000Z | Reference the Sionna documentation for APIs, tutorials, and examples. | Sionna Documentation | Technical - Intermediate | Documentation |
| NVIDIA Aerial Omniverse Digital Twin Boosts Development of AI-Native Wireless and Deployment Flexibility | false | blog | https://developer.nvidia.com/blog/nvidia-aerial-omniverse-digital-twin-boosts-development-of-ai-native-wireless-and-deployment-flexibility/ | Aerial Omniverse Digital Twin | 2025-03-19T04:00:00.000Z | Explore how the Aerial Omniverse Digital Twin accelerates AI-native wireless development and deployment. | NVIDIA Aerial Omniverse Digital Twin Boosts Development of AI-Native Wireless and Deployment Flexibility | Technical - Intermediate | Explainer |
| AI-RAN Goes Live and Unlocks a New AI Opportunity for Telcos | false | blog | https://developer.nvidia.com/blog/ai-ran-goes-live-and-unlocks-a-new-ai-opportunity-for-telcos/ | AI Aerial | 2024-11-12T05:00:00.000Z | Explore how AI-RAN converges AI and RAN workloads on shared infrastructure to unlock new telco revenue. | AI-RAN Goes Live and Unlocks a New AI Opportunity for Telcos | Technical - Intermediate | Explainer |
| Automating Telco Network Design using NVIDIA NIM and NVIDIA NeMo | false | blog | https://developer.nvidia.com/blog/automating-telco-network-design-using-nvidia-nim-and-nvidia-nemo/ | NeMo, NIM | 2024-07-23T04:00:00.000Z | Automate telco network design using NVIDIA NIM and NeMo to generate TOSCA templates. | Automating Telco Network Design using NVIDIA NIM and NVIDIA NeMo | Technical - Intermediate | Tutorial |
| Design and Test 5G and 6G Networks Using NVIDIA Aerial Omniverse Digital Twin | false | video | https://www.youtube.com/watch?v=J5-rkgL2dFA | Aerial Omniverse Digital Twin | 2024-04-23T04:00:00.000Z | See how AODT designs and tests 5G/6G networks with high-fidelity ray-traced simulation. | Design and Test 5G and 6G Networks Using NVIDIA Aerial Omniverse Digital Twin | Technical - Beginner | Demo |
| Building Sovereign AI Models — Technical Overview | false | pdf | https://nvdam.widen.net/s/zqhnctvvnb/sovereign-ai-technical-overview | NeMo Curator, NeMo Guardrails, Nemotron, NIM, TensorRT-LLM | 2024-10-10T04:00:00.000Z | Learn end-to-end sovereign LLM development covering data curation, training, safety, and deployment. | Building Sovereign AI Models — Technical Overview | Technical - Intermediate | Explainer |
| Build With NVIDIA AI Grid Reference Design | false | webpage | https://docs.nvidia.com/ai-grid/whitepapers/ai-grid-reference-design/ | AI Grid Reference Architecture | 2026-03-17T04:00:00.000Z | Reference the AI Grid design whitepaper guiding service providers deploying distributed AI-native services. | Build With NVIDIA AI Grid Reference Design | Technical - Advanced | Documentation |
| Sionna RT: Scene Creation with Blender using OpenStreetMap | false | video | https://www.youtube.com/watch?v=7xHLDxUaQ7c | Sionna | 2023-03-21T04:00:00.000Z | Build Sionna RT scenes in Blender from OpenStreetMap data for ray-traced wireless simulation. | Sionna RT: Scene Creation with Blender using OpenStreetMap | Technical - Intermediate | Tutorial |
| NVIDIA AI Grid Documentation | false | webpage | https://docs.nvidia.com/ai-grid/index.html | AI Grid Reference Architecture | 2026-03-17T04:00:00.000Z | Reference the AI Grid docs covering geographically distributed AI infrastructure as a unified platform. | NVIDIA AI Grid Documentation | Technical - Intermediate | Documentation |
| How NVIDIA AODT Powers Network Digital Twins | true | video | https://www.youtube.com/watch?v=BGV1vZ9lX4s | Aerial Omniverse Digital Twin | 2026-03-17T04:00:00.000Z | Watch how AODT powers wireless network digital twins for AI-native 6G research. | How NVIDIA AODT Powers Network Digital Twins | Technical - Beginner | Demo |
| NVlabs/sionna-rk — Sionna Research Kit (GitHub) | false | code | https://github.com/NVlabs/sionna-rk | Sionna | 2026-03-19T04:00:00.000Z | Fork the Sionna Research Kit: a DGX Spark + OpenAirInterface platform for AI-native RAN experimentation. | NVlabs/sionna-rk — Sionna Research Kit (GitHub) | Technical - Intermediate | Samples |
| Enhanced DU Performance and Workload Consolidation for 5G/6G with NVIDIA Aerial CUDA-Accelerated RAN | false | blog | https://developer.nvidia.com/blog/enhanced-du-performance-and-workload-consolidation-for-5g-6g-with-aerial-cuda-accelerated-ran/ | Aerial CUDA Accelerated RAN | 2024-04-22T04:00:00.000Z | Explore enhanced DU performance and workload consolidation for 5G/6G using Aerial CUDA-Accelerated RAN. | Enhanced DU Performance and Workload Consolidation for 5G/6G with NVIDIA Aerial CUDA-Accelerated RAN | Technical - Advanced | Explainer |
| Advanced RAG Techniques for Telco O-RAN Specifications Using NVIDIA NIM Microservices | false | blog | https://developer.nvidia.com/blog/advanced-rag-techniques-for-telco-o-ran-specifications-using-nvidia-nim-microservices/ | NeMo Retriever, NIM | 2024-10-10T04:00:00.000Z | Build advanced RAG pipelines over O-RAN specifications using NVIDIA NIM microservices. | Advanced RAG Techniques for Telco O-RAN Specifications Using NVIDIA NIM Microservices | Technical - Advanced | Tutorial |
| Building the AI Grid with NVIDIA: Orchestrating Intelligence Everywhere | false | blog | https://developer.nvidia.com/blog/building-the-ai-grid-with-nvidia-orchestrating-intelligence-everywhere/ | AI Grid Reference Architecture, NIM | 2026-03-17T04:00:00.000Z | Explore the NVIDIA AI Grid reference for orchestrating inference across distributed telco infrastructure. | Building the AI Grid with NVIDIA: Orchestrating Intelligence Everywhere | Technical - Intermediate | Explainer |
| Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo | false | blog | https://developer.nvidia.com/blog/building-telco-reasoning-models-for-autonomous-networks-with-nvidia-nemo/ | NeMo Framework | 2026-02-28T05:00:00.000Z | Build telco reasoning models for autonomous networks using the NVIDIA NeMo framework. | Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo | Technical - Advanced | Tutorial |
| Jumpstarting Link-Level Simulations with NVIDIA Sionna | false | blog | https://developer.nvidia.com/blog/jumpstarting-link-level-simulations-with-sionna/ | Sionna | 2022-03-22T04:00:00.000Z | Build GPU-accelerated link-level wireless simulations using the open-source NVIDIA Sionna library. | Jumpstarting Link-Level Simulations with NVIDIA Sionna | Technical - Intermediate | Tutorial |
| Aerial Omniverse Digital Twin Documentation | false | webpage | https://docs.nvidia.com/aerial/aerial-dt/index.html | Aerial Omniverse Digital Twin | 2025-03-19T04:00:00.000Z | Reference the Aerial Omniverse Digital Twin documentation for installation, APIs, and scene authoring. | Aerial Omniverse Digital Twin Documentation | Technical - Intermediate | Documentation |
| Accelerating the Path to 6G With an AI-Native Wireless Stack | false | video | https://www.youtube.com/watch?v=f40DlANmJmo | AI Aerial | 2025-10-28T04:00:00.000Z | See how the NVIDIA AI-native wireless stack accelerates the path to 6G. | Accelerating the Path to 6G With an AI-Native Wireless Stack | Technical - Beginner | Overview |
| NVIDIA Aerial Framework Documentation | true | webpage | https://docs.nvidia.com/aerial/framework/latest/ | Aerial Framework | 2025-12-10T05:00:00.000Z | Reference the Aerial Framework docs for real-time signal-processing pipelines with microsecond latency. | NVIDIA Aerial Framework Documentation | Technical - Intermediate | Documentation |
| aerial-cuda-accelerated-ran (GitHub) | false | code | https://github.com/NVIDIA/aerial-cuda-accelerated-ran | Aerial CUDA Accelerated RAN | 2026-04-22T04:00:00.000Z | Fork the open-source Aerial CUDA-Accelerated RAN 5G/6G physical-layer stack. | aerial-cuda-accelerated-ran (GitHub) | Technical - Beginner | Samples |
| Transforming Telco Network Operations Centers with NVIDIA NeMo Retriever and NVIDIA NIM | false | blog | https://developer.nvidia.com/blog/transforming-telco-network-operations-centers-with-nvidia-nemo-retriever-and-nvidia-nim/ | NeMo Retriever, NIM | 2024-07-23T04:00:00.000Z | Build agentic NOC assistants that triage incidents using NeMo Retriever and NIM microservices. | Transforming Telco Network Operations Centers with NVIDIA NeMo Retriever and NVIDIA NIM | Technical - Intermediate | Tutorial |
| Powering AI-Native 6G Research with the NVIDIA Sionna Research Kit | false | blog | https://developer.nvidia.com/blog/powering-ai-native-6g-research-with-the-nvidia-sionna-research-kit/ | Sionna | 2025-10-28T04:00:00.000Z | Explore the Sionna Research Kit for hardware-in-the-loop AI-native 6G research on DGX Spark. | Powering AI-Native 6G Research with the NVIDIA Sionna Research Kit | Technical - Intermediate | Explainer |
| Aerial CUDA-Accelerated RAN Documentation | false | webpage | https://docs.nvidia.com/aerial/cuda-accelerated-ran/latest/index.html | Aerial CUDA Accelerated RAN | 2024-04-22T04:00:00.000Z | Reference the Aerial CUDA-Accelerated RAN documentation for installation, configuration, and APIs. | Aerial CUDA-Accelerated RAN Documentation | Technical - Intermediate | Documentation |
| Aerial Framework Python Development Guide | false | webpage | https://docs.nvidia.com/aerial/framework/latest/developer_guide/python.html | Aerial Framework | 2025-12-10T05:00:00.000Z | Reference the Aerial Framework Python guide covering JAX PHY, MLIR-TensorRT lowering, and Sionna datasets. | Aerial Framework Python Development Guide | Technical - Intermediate | Documentation |
| NVIDIA Open Sources Aerial Software to Accelerate AI-Native 6G | true | blog | https://blogs.nvidia.com/blog/open-source-aerial-ai-native-6g/ | AI Aerial | 2025-10-28T04:00:00.000Z | Discover how NVIDIA's open-sourced Aerial software accelerates AI-native 6G research and development. | NVIDIA Open Sources Aerial Software to Accelerate AI-Native 6G | Technical - Beginner | News |
| AI Agent for Telecom Network Configuration Planning | false | hands-on | https://build.nvidia.com/nvidia/telco-network-configuration | NIM | 2024-10-10T04:00:00.000Z | Try an agentic AI blueprint that automates and optimizes RAN parameter configuration with LLMs. | AI Agent for Telecom Network Configuration Planning | Technical - Intermediate | Demo |
| NVIDIA Aerial Omniverse Digital Twin | false | webpage | https://developer.nvidia.com/aerial-omniverse-digital-twin | Aerial Omniverse Digital Twin | 2025-03-19T04:00:00.000Z | Get introduced to the Aerial Omniverse Digital Twin for high-fidelity wireless simulation. | NVIDIA Aerial Omniverse Digital Twin | Technical - Beginner | Overview |
| pyAerial Documentation | false | webpage | https://docs.nvidia.com/aerial/cuda-accelerated-ran/latest/pyaerial/index.html | Aerial CUDA Accelerated RAN | 2024-04-22T04:00:00.000Z | Reference pyAerial APIs for Python-based physical-layer simulation and prototyping. | pyAerial Documentation | Technical - Intermediate | Documentation |
| Part 1: Getting Started with Sionna | true | hands-on | https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Sionna_tutorial_part1.html | Sionna | 2022-03-22T04:00:00.000Z | Build a 5G NR point-to-point link in Sionna and train a neural receiver end-to-end. | Part 1: Getting Started with Sionna | Technical - Beginner | Tutorial |
| NVlabs/sionna (GitHub) | false | code | https://github.com/NVlabs/sionna | Sionna | 2026-04-01T04:00:00.000Z | Fork Sionna 2.0: open-source ray tracer (RT), link-level simulator (PHY), and system-level simulator (SYS). | NVlabs/sionna (GitHub) | Technical - Beginner | Samples |
| NVIDIA AI Aerial Documentation | false | webpage | https://docs.nvidia.com/aerial/index.html | AI Aerial | 2025-10-28T04:00:00.000Z | Reference NVIDIA AI Aerial documentation for the full software-defined 5G/6G stack. | NVIDIA AI Aerial Documentation | Technical - Intermediate | Documentation |
| Maximize Spectral Efficiency with AI-Native RAN and NVIDIA AI Aerial | false | blog | https://developer.nvidia.com/blog/maximize-spectral-efficiency-with-ai-native-ran-and-nvidia-ai-aerial | AI Aerial | 2026-07-06T00:00:00.000Z | Learn about the benefits of GPU acceleration in the RAN, and how AI-native RAN helps close the massive MIMO performance gap. It shows how NVIDIA AI Aerial enables a new class of Layer 1 and Layer 2 algorithms designed to unlock greater spectral efficiency in real-world deployments. | Maximize Spectral Efficiency with AI-Native RAN and NVIDIA AI Aerial | Technical - Intermediate | Explainer |
| Five Takeaways from NVIDIA 6G Developer Day 2024 | false | blog | https://developer.nvidia.com/blog/five-takeaways-from-nvidia-6g-developer-day-2024/ | AI Aerial | 2024-12-14T00:00:00.000Z | Survey key 6G research themes and NVIDIA tooling announcements from 6G Developer Day 2024. | Five Takeaways from NVIDIA 6G Developer Day 2024 | Technical - Beginner | Overview |
| cuBB Developer Guide | false | webpage | https://docs.nvidia.com/aerial/cuda-accelerated-ran/latest/cubb/index.html | Aerial CUDA Accelerated RAN | 2024-04-21T00:00:00.000Z | Reference the cuBB guide covering cuPHY, cuMAC, and pyAerial for 5G gNB L1 development. | cuBB Developer Guide | Technical - Advanced | Documentation |

[Download the raw results data (JSON)](https://developer.nvidia.com/search-data/telecommunications.json)


* * *

## NVIDIA AI Aerial Ecosystem

Academia and industry leaders are collaborating to advance AI-native wireless networks and drive 6G research.

![NVIDIA AI Aerial Ecosystem and Industry partner -](https://developer.download.nvidia.com/images/logos/1finity-fujitsu-logo.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner - Aarna.mi](https://developer.download.nvidia.com/images/logos/aarna-logo.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner - Amdocs](https://developer.download.nvidia.com/images/logos/amdocs-logo.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner - Ansys](https://developer.download.nvidia.com/images/logos/endorsed-ansys-logos-full-color-rgb.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner -](https://developer.download.nvidia.com/images/logos/gtc-dc-rgb-booz-allen-1250x703.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner - Cisco](https://developer.download.nvidia.com/images/logos/cisco-logo.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner -](https://developer.download.nvidia.com/images/logos/deepsig-logo.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner -](https://developer.download.nvidia.com/images/logos/dell-logo.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner -](https://developer.download.nvidia.com/images/logos/eth-zurich-logo.svg)

![NVIDIA AI Aerial Ecosystem and Industry partner -](https://developer.download.nvidia.com/images/logos/hpe-logo.svg)

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## Next Steps

## Join the 6G Developer Program  

Get access to NVIDIA AI Aerial resources and community. 

[Join Now](https://developer.nvidia.com/6g-program "Join Now")

## NVIDIA AI Aerial FAQ

NVIDIA AI Aerial is a suite of accelerated computing platforms, software libraries, and tools for building, training, simulating, and deploying AI-native wireless networks. AI Aerial supports both advanced wireless research and commercial-grade cellular deployment. It enables developers to go from rapid prototyping to commercial development of AI-RAN solutions for 5G and 6G that telcos can deploy.

AI Aerial includes a variety of hardware and software resources for wireless research and commercial deployment. Hardware ranges from small to large accelerated computing platforms, comprising various GPUs, CPUs, and networking components. Software libraries range from open source Sionna for rapid prototyping to Aerial CUDA-Accelerated RAN, Aerial Framework, and Aerial Omniverse Digital Twin for building CUDA-accelerated software-defined RAN.

Developers and researchers can access all NVIDIA Aerial software on GitHub: [NVIDIA Sionna](https://github.com/NVlabs/sionna), [NVIDIA Aerial CUDA-Accelerated RAN](https://github.com/NVIDIA/aerial-cuda-accelerated-ran), [NVIDIA Aerial Framework](https://github.com/NVIDIA/aerial-framework), and [NVIDIA Aerial Omniverse Digital Twin Client](https://github.com/NVIDIA/aerial-omniverse-digital-twin). For complete AODT software, access is available through the NVIDIA 6G Developer Program. Documentation, SDKs, and hardware platforms are available for rapid onboarding and experimentation.

Compatible hardware includes Aerial Testbed (ARC-OTA), Aerial RAN Computer-1, ARC-Compact, ARC-Pro, and the Sionna Research Kit. These are built using various accelerated computing platforms such as NVIDIA Jetson Orin™, DGX Spark, Grace Hopper 200 for research, and Grace Blackwell 300, L4 GPUs, and RTX PRO GPUs for commercial deployments.

AI Aerial supports centralized and distributed AI-RAN deployments for public and private 5G and future 6G networks. Aerial CUDA-Accelerated RAN is O-RAN 7.2x compliant and supports O-RAN Distributed Unit (O-DU) functionality.

For specific questions about NVIDIA Aerial CUDA-Accelerated RAN, please ask in the [GitHub issues](https://github.com/NVIDIA/aerial-cuda-accelerated-ran/issues) in the repo. For specific questions about NVIDIA Aerial Frameworks, please ask in the [GitHub issues](https://github.com/NVIDIA/aerial-framework) in the repo. For all other questions, please ask in the [Aerial Developer Forum](https://forums.developer.nvidia.com/c/accelerated-computing/aerial/674).