NVIDIA Documentation Hub

NVIDIA’s program that enables enterprises to confidently deploy hardware solutions that optimally run accelerated workloads—from desktop to data center to edge.

The NVIDIA-managed cuOpt service is a high-performance, on-demand routing optimization service fully managed by NVIDIA.

CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA.

The NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, for accelerating the pre-processing of input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications.

Deep Graph Library (DGL) is a framework-neutral, easy-to-use, and scalable Python library used for implementing and training Graph Neural Networks (GNN). Being framework-neutral, DGL is easily integrated into an existing PyTorch, TensorFlow, or an Apache MXNet workflow.

The NVIDIA Deep Learning GPU Training System (DIGITS) can be used to rapidly train highly accurate deep neural networks (DNNs) for image classification, segmentation, and object-detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best-performing model from the results browser for deployment.

The NVIDIA HGX™ platform brings together the full power of NVIDIA GPUs, NVIDIA NVLink™, NVIDIA networking, and fully optimized AI and high-performance computing (HPC) software stacks to provide the highest application performance and drive the fastest time to insights for every data center.

GB200 NVL72 connects 36 Grace CPUs and 72 Blackwell GPUs using a 72-GPU NVLink domain in a rack-scale, liquid-cooled design.

NVIDIA NGC is the hub for GPU-optimized software for deep learning, machine learning, and HPC that provides containers, models, model scripts, and industry solutions so data scientists, developers and researchers can focus on building solutions and gathering insights faster.

The Programmable Vision Accelerator Software Development Kit (PVA SDK) from NVIDIA empowers developers to write, debug, and optimize applications for the PVA engine available on NVIDIA DRIVE and Jetson platforms. NVIDIA PVA SDK includes PVA Solutions, a repository of optimized algorithms for the PVA SDK which are made available as source code to PVA SDK users.

The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA CUDA primitives and high-bandwidth GPU memory under the hood.

The RAPIDS Accelerator for Apache Spark leverages GPUs to accelerate processing by combining the power of the RAPIDS cuDF library and the scale of the Spark distributed computing framework. You can run your existing Apache Spark applications on GPUs with no code change by launching Spark with the RAPIDS Accelerator for Apache Spark plugin jar and enabling a single configuration setting.

GPU-accelerated enhancements to gradient boosting library XGBoost to provide fast and accurate ways to solve large-scale AI and data science problems.