Developer Resources For Telecommunications

NVIDIA Aerial is an application framework for building high-performance, software-defined, GPU-accelerated 5G virtual radio access networks (RANs). NVIDIA Aerial provides two critical SDKs—cuVNF and cuBB—to help you optimize your results with parallel processing on GPU for baseband signals and data flow.

The NVIDIA CloudXR™ SDK enables streaming of graphics-intensive augmented reality, virtual reality, or mixed reality content—often called extended reality (XR)—over a radio signal (5G or Wi-Fi) to one or more devices. The SDK also enables streaming of Open VR applications to a number of 5G-connected Android devices.

The CUDA® parallel computing platform and programming model enables developers to dramatically speed up computing applications by harnessing the power of GPUs. The CUDA Toolkit provides everything you need to develop GPU-accelerated applications.

The RAPIDS suite of open-source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA and based on extensive hardware and data science experience.

NVIDIA TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications.

NVIDIA Triton Inference Server maximizes inference utilization and performance on GPUs via an HTTP or gRPC endpoint, allowing remote clients to request inference for any model that’s being managed by the server.

The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines, such as forward and backward convolution, pooling, normalization, and activation layers.

NVIDIA JetPack™ SDK is the most comprehensive solution for building AI applications. It includes the latest OS images for Jetson™ products, along with libraries and APIs, samples, developer tools, and documentation.

Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the power of GPUs using the most essential CUDA techniquest and the NVIDIA Nsight™ System profiler.

Reduce complexity and improve portability and efficiency of your code by using a containerized environment for high-performance computing (HPC) application development.

Explore an introduction to AI, GPU computing, NVIDIA AI software architecture, and how to implement and scale AI workloads in the data center. You'll understand how AI is transforming society and how to deploy GPU computing to the data center to facilitate this transformation.