NVIDIA Documentation Hub

Clara Parabricks is a complete software solution for next-generation sequencing, including short- and long-read applications, supporting workflows that start with basecalling and extend through tertiary analysis. Clara Parabricks Pipelines were built to optimize acceleration, accuracy, and scalability. Users can achieve a 35-50X acceleration and 99.99 percent accuracy for variant calling when comparing against CPU-only BWA-GATK4 pipelines. It can run the full GATK4 Best Practices and is also fully configurable, letting users choose which steps, parameter settings, and versions of the pipeline to run.

The integration of NVIDIA RAPIDS into the Cloudera Data Platform (CDP) provides transparent GPU acceleration of data analytics workloads using Apache Spark. This documentation describes the integration and suggested reference architectures for deployment.

This documentation should be of interest to cluster admins and support personnel of enterprise GPU deployments. It includes monitoring and management tools and application programming interfaces (APIs), in-field diagnostics and health monitoring, and cluster setup and deployment.

With our expansive support tiers, fast implementations, robust professional services, market-leading education, and high caliber technical certifications, we are here to help you achieve success with all parts of NVIDIA’s accelerated computing, visualization, and networking platform.

Manuals of NIC Drivers, FW, Accelerators, Switch OS, Management, Tools for Networking.

nvCOMP is a high performance GPU enabled data compression library. Includes both open-source and non-OS components. The nvCOMP library provides fast lossless data compression and decompression using a GPU. It features generic compression interfaces to enable developers to use high-performance GPU compressors in their applications.

The software layer of the NVIDIA AI platform, NVIDIA AI Enterprise, accelerates the data science pipeline and streamlines the development and deployment of production AI including generative AI, computer vision, speech AI and more. With over 50 frameworks, pre-trained models, and development tools, NVIDIA AI Enterprise is designed to accelerate enterprises to the leading edge of AI while simplifying AI to make it accessible to every enterprise.

NVIDIA Ansel is a revolutionary way to capture in-game shots and share the moment. Compose your screenshots from any position, adjust them with post-process filters, capture HDR images in high-fidelity formats, and share them in 360 degrees using your mobile phone, PC, or VR headset.

NVIDIA Base Command Platform is a world-class infrastructure solution for businesses and their data scientists who need a premium AI development experience.

NVIDIA Base OS implements the stable and fully qualified operating systems for running AI, machine learning, and analytics applications on the DGX platform. It includes system-specific configurations, drivers, and diagnostic and monitoring tools and is available for Ubuntu, Red Hat Enterprise Linux, and Rocky Linux.

NVIDIA Bright Cluster Manager offers fast deployment and end-to-end management for heterogeneous HPC and AI server clusters at the edge, in the data center and in multi/hybrid-cloud environments. It automates provisioning and administration for clusters ranging in size from a single node to hundreds of thousands, supports CPU-based and NVIDIA GPU-accelerated systems, and orchestration with Kubernetes.

NVIDIA Capture SDK (formerly GRID SDK) enables developers to easily and efficiently capture, and optionally encode, the display content.

NVIDIA® Clara™ is an open, scalable computing platform that enables developers to build and deploy medical imaging applications into hybrid (embedded, on-premises, or cloud) computing environments to create intelligent instruments and automate healthcare workflows.

NVIDIA Clara Viz is a platform for visualization of 2D/3D medical imaging data. The core of this platform is the Clara Viz SDK, which is designed to enable developers to incorporate high performance volumetric visualization of medical images in applications with an easy-to-use API.

NVIDIA cloud-native technologies enable developers to build and run GPU-accelerated containers using Docker and Kubernetes.

CloudXR is NVIDIA's solution for streaming virtual reality (VR), augmented reality (AR), and mixed reality (MR) content from any OpenVR XR application on a remote server--desktop, cloud, data center, or edge.

Compute Sanitizer is a functional correctness checking suite included in the CUDA toolkit. This suite contains multiple tools that can perform different type of checks. The memcheck tool is capable of precisely detecting and attributing out of bounds and misaligned memory access errors in CUDA applications. The tool can also report hardware exceptions encountered by the GPU. The racecheck tool can report shared memory data access hazards that can cause data races. The initcheck tool can report cases where the GPU performs uninitialized accesses to global memory. The synccheck tool can report cases where the application is attempting invalid usages of synchronization primitives. This document describes the usage of these tools.

The NVIDIA® CUDA® Toolkit provides a comprehensive development environment for C and C++ developers building GPU-accelerated applications. With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers.

The NVIDIA® CUDA Deep Neural Network (cuDNN) library 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. Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration.

NVIDIA cuOpt is an Operations Research optimization API using AI to help developers create complex, real-time fleet routing workflows on NVIDIA GPUs.

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.

NVIDIA Data Center GPU drivers are used in Data Center GPU enterprise deployments for AI, HPC, and accelerated computing workloads. Documentation includes release notes, supported platforms, and cluster setup and deployment.

NVIDIA Data Center GPU Manager (DCGM) is a suite of tools for managing and monitoring NVIDIA Data Center GPUs in cluster environments.

NVIDIA Data Science Workbench is a productivity tool for GPU-enabled workstations to improve manageability, reproducibility, and usability for data scientists, data engineers, and AI developers. Users have fast and convenient access to a plethora of data science tools and CLIs while also benefiting from easy installation and updating stack software.

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.

GPUs accelerate machine learning operations by performing calculations in parallel. Many operations, especially those representable as matrix multipliers will see good acceleration right out of the box. Even better performance can be achieved by tweaking operation parameters to efficiently use GPU resources. The performance documents present the tips that we think are most widely useful.

Built from the ground up for enterprise AI, the NVIDIA DGX platform incorporates the best of NVIDIA software, infrastructure, and expertise in a modern, unified AI development and training solution. Every aspect of the DGX platform is infused with NVIDIA AI expertise, featuring world-class software, record-breaking NVIDIA-accelerated infrastructure in the cloud or on-premises, and direct access to NVIDIA DGXPerts to speed the ROI of AI for every enterprise.

System documentation for the DGX AI supercomputers that deliver world-class performance for large generative AI and mainstream AI workloads.

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.

Learn how to develop for NVIDIA DRIVE®, a scalable computing platform that enables automakers and Tier-1 suppliers to accelerate production of autonomous vehicles.

The NVIDIA EGX platform delivers the power of accelerated AI computing to the edge with a cloud-native software stack (EGX stack), a range of validated servers and devices, Helm charts, and partners who offer EGX through their products and services.

FLARE (Federated Learning Active Runtime Environment) is Nvidia’s open source extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflow to a privacy preserving federated paradigm. FLARE makes it possible to build robust, generalizable AI models without sharing data.

NVIDIA Fleet Command brings secure edge AI to enterprises of any size. Transform NVIDIA-certified servers into secure edge appliances and connect them to the cloud in minutes. From the cloud, deploy and manage applications from the NGC Catalog or your NGC Private Registry, update system software over-the-air and manage systems remotely with nothing but a browser and internet connection.

Documentation for GameWorks-related products and technologies, including libraries (NVAPI, OpenAutomate), code samples (DirectX, OpenGL), and developer tools (Nsight, NVIDIA System Profiler).

The GeForce NOW Developer Platform is an SDK and toolset empowering integration of, interaction with, and testing on the NVIDIA cloud gaming service.

NVIDIA GPUDirect Storage (GDS) enables the fastest data path between GPU memory and storage by avoiding copies to and from system memory, thereby increasing storage input/output (IO) bandwidth and decreasing latency and CPU utilization.

NVIDIA GVDB Voxels is a new framework for simulation, compute and rendering of sparse voxels on the GPU.

NVIDIA Highlights enables automatic video capture of key moments, clutch kills, and match-winning plays, ensuring gamers’ best gaming moments are always saved. Once a Highlight is captured, gamers can simply share it directly to Facebook, YouTube, or Weibo right from GeForce Experience’s in-game overlay. Additionally, they can also clip their favorite 15 seconds and share as an animated GIF - all without leaving the game!

The NVIDIA HPC SDK is a comprehensive suite of compilers, libraries, and development tools used for developing HPC applications for the NVIDIA platform.

NVIDIA IGX Orin™ is an industrial-grade platform that combines enterprise-level hardware, software, and support. As a single, holistic platform, IGX allows companies to focus on application development and realize the benefits of AI faster.

NVIDIA IndeX is a 3D volumetric interactive visualization SDK that allows scientists and researchers to visualize and interact with massive data sets, make real-time modifications, and navigate to the most pertinent parts of the data, all in real-time, to gather better insights faster. IndeX leverages GPU clusters for scalable, real-time, visualization and computing of multi-valued volumetric data together with embedded geometry data.

NVIDIA Isaac is a developer toolbox for accelerating the development and deployment of AI-powered robots. The SDK includes Isaac applications, GEMs (robot capabilities), a Robot Engine, and NVIDIA Isaac Sim.

NVIDIA virtual GPU (vGPU) software is a graphics virtualization platform that extends the power of NVIDIA GPU technology to virtual desktops and apps, offering improved security, productivity, and cost-efficiency.

NVIDIA® Clara™ is an open, scalable computing platform that enables developers to build and deploy medical imaging applications into hybrid (embedded, on-premises, or cloud) computing environments to create intelligent instruments and automate healthcare workflows.

NVIDIA Triton Inference Server (formerly TensorRT Inference Server) provides a cloud inferencing solution optimized for NVIDIA GPUs. The server provides an inference service via an HTTP or gRPC endpoint, allowing remote clients to request inferencing for any model being managed by the server.

NVIDIA Maxine is a GPU-accelerated SDK with state-of-the-art AI features for developers to build virtual collaboration and content creation applications such as video conferencing and live streaming. Maxine’s AI SDKs, such as Video Effects, Audio Effects, and Augmented Reality (AR) are highly optimized and include modular features that can be chained into end-to-end pipelines to deliver the highest performance possible on GPUs, both on PCs and in data centers.

NVIDIA LaunchPad is a free program that provides users short-term access to a large catalog of hands-on labs. Now enterprises and organizations can immediately tap into the necessary hardware and software stacks to experience end-to-end solution workflows in the areas of AI, data science, 3D design collaboration and simulation, and more.

The GeForce NOW Developer Platform is an SDK and toolset empowering integration of, interaction with, and testing on the NVIDIA cloud gaming service.

VRWorks™ is a comprehensive suite of APIs, libraries, and engines that enable application and headset developers to create amazing virtual reality experiences. VRWorks enables a new level of presence by bringing physically realistic visuals, sound, touch interactions, and simulated environments to virtual reality.

NVIDIA Data Center GPU Manager (DCGM) is a suite of tools for managing and monitoring NVIDIA Data Center GPUs in cluster environments.

Metropolis Microservices is a suite of cloud-native microservices and reference applications to fast-track the development and deployment of vision AI applications from edge to cloud.

Deep learning (DL) frameworks offer building blocks for designing, training, and validating deep neural networks through a high-level programming interface. Widely-used DL frameworks, such as PyTorch, TensorFlow, PyTorch Geometric, DGL, and others, rely on GPU-accelerated libraries, such as cuDNN, NCCL, and DALI to deliver high-performance, multi-GPU-accelerated training.

NVIDIA Neural Modules (NeMo) is a flexible, Python-based toolkit enabling data scientists and researchers to build state-of-the-art speech and language deep learning models composed of reusable building blocks that can be safely connected together for conversational AI applications.

NVIDIA GVDB Voxels is a new framework for simulation, compute and rendering of sparse voxels on the GPU.

The software layer of the NVIDIA AI platform, NVIDIA AI Enterprise, accelerates the data science pipeline and streamlines the development and deployment of production AI including generative AI, computer vision, speech AI and more. With over 50 frameworks, pre-trained models, and development tools, NVIDIA AI Enterprise is designed to accelerate enterprises to the leading edge of AI while simplifying AI to make it accessible to every enterprise.

NVIDIA MAGNUM IO™ software development kit (SDK) enables developers to remove input/output (IO) bottlenecks in AI, high performance computing (HPC), data science, and visualization applications, reducing the end-to-end time of their workflows. Magnum IO covers all aspects of data movement between CPUs, GPUsns, DPUs, and storage subsystems in virtualized, containerized, and bare-metal environments.

The NVIDIA® CUDA® Toolkit provides a comprehensive development environment for C and C++ developers building GPU-accelerated applications. With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers.

The integration of NVIDIA RAPIDS into the Cloudera Data Platform (CDP) provides transparent GPU acceleration of data analytics workloads using Apache Spark. This documentation describes the integration and suggested reference architectures for deployment.

The NVIDIA TAO Toolkit eliminates the time-consuming process of building and fine-tuning DNNs from scratch for IVA applications.

Manuals of NIC Drivers, FW, Accelerators, Switch OS, Management, Tools for Networking.

NVIDIA SDK Manager is an all-in-one tool that bundles developer software and provides an end-to-end development environment setup solution for NVIDIA SDKs. Learn about the prerequisite hardware and software to get started with NVIDIA SDK Manager. See the latest features and updates.

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

NVIDIA Ansel is a revolutionary way to capture in-game shots and share the moment. Compose your screenshots from any position, adjust them with post-process filters, capture HDR images in high-fidelity formats, and share them in 360 degrees using your mobile phone, PC, or VR headset.

NVIDIA® License System is used to serve a pool of floating licenses to NVIDIA licensed products. The NVIDIA License System is configured with licenses obtained from the NVIDIA Licensing Portal.

FLARE (Federated Learning Active Runtime Environment) is Nvidia’s open source extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflow to a privacy preserving federated paradigm. FLARE makes it possible to build robust, generalizable AI models without sharing data.

NVIDIA Base OS implements the stable and fully qualified operating systems for running AI, machine learning, and analytics applications on the DGX platform. It includes system-specific configurations, drivers, and diagnostic and monitoring tools and is available for Ubuntu, Red Hat Enterprise Linux, and Rocky Linux.

NVIDIA Base Command Platform is a world-class infrastructure solution for businesses and their data scientists who need a premium AI development experience.

NVIDIA Topology-Aware GPU Selection (NVTAGS) intelligently and automatically assigns GPUs to MPI processes, which reduces overall GPU-to-GPU communication time for Message Passing Interface (MPI) applications.

Unique IP-based solution that boosts video and data streaming performance. Rivermax together with NVIDIA GPU accelerated computing technologies unlocks innovation for a wide range of applications in Media and Entertainment (M&E), Broadcast, Healthcare, Smart Cities and more.

The NVIDIA Virtual Reality Capture and Replay (VCR) SDK enables developers and users to accurately capture and replay VR sessions for performance testing, scene troubleshooting, and more.

System documentation for the DGX AI supercomputers that deliver world-class performance for large generative AI and mainstream AI workloads.

NVIDIA Capture SDK (formerly GRID SDK) enables developers to easily and efficiently capture, and optionally encode, the display content.

nvCOMP is a high performance GPU enabled data compression library. Includes both open-source and non-OS components. The nvCOMP library provides fast lossless data compression and decompression using a GPU. It features generic compression interfaces to enable developers to use high-performance GPU compressors in their applications.

NVIDIA Data Science Workbench is a productivity tool for GPU-enabled workstations to improve manageability, reproducibility, and usability for data scientists, data engineers, and AI developers. Users have fast and convenient access to a plethora of data science tools and CLIs while also benefiting from easy installation and updating stack software.

Built from the ground up for enterprise AI, the NVIDIA DGX platform incorporates the best of NVIDIA software, infrastructure, and expertise in a modern, unified AI development and training solution. Every aspect of the DGX platform is infused with NVIDIA AI expertise, featuring world-class software, record-breaking NVIDIA-accelerated infrastructure in the cloud or on-premises, and direct access to NVIDIA DGXPerts to speed the ROI of AI for every enterprise.

NVIDIA Clara Viz is a platform for visualization of 2D/3D medical imaging data. The core of this platform is the Clara Viz SDK, which is designed to enable developers to incorporate high performance volumetric visualization of medical images in applications with an easy-to-use API.

NVIDIA works with Facebook and the community to accelerate PyTorch on NVIDIA GPUs in the main PyTorch branch, as well as, with ready-to-run containers in NGC.

NVIDIA Modulus is a deep learning framework that blends the power of physics and partial differential equations (PDEs) with AI to build more robust models for better analysis.

NVIDIA Bright Cluster Manager offers fast deployment and end-to-end management for heterogeneous HPC and AI server clusters at the edge, in the data center and in multi/hybrid-cloud environments. It automates provisioning and administration for clusters ranging in size from a single node to hundreds of thousands, supports CPU-based and NVIDIA GPU-accelerated systems, and orchestration with Kubernetes.

The NVIDIA MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.

NVIDIA NGX makes it easy to integrate pre-built, AI-based features into applications with the NGX SDK, NGX Core Runtime and NGX Update Module. The NGX infrastructure updates the AI-based features on all clients that use it.

Learn how to develop for NVIDIA DRIVE®, a scalable computing platform that enables automakers and Tier-1 suppliers to accelerate production of autonomous vehicles.

GPUs accelerate machine learning operations by performing calculations in parallel. Many operations, especially those representable as matrix multipliers will see good acceleration right out of the box. Even better performance can be achieved by tweaking operation parameters to efficiently use GPU resources. The performance documents present the tips that we think are most widely useful.

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 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 Material Definition Language (MDL) is a programming language for defining physically based materials for rendering. The MDL SDK is a set of tools to integrate MDL support into rendering applications. It contains components for loading, inspecting, editing of material definitions as well as compiling MDL functions to GLSL, HLSL, Native x86, PTX and LLVM-IR. With the NVIDIA MDL SDK, any physically based renderer can easily add support for MDL and join the MDL eco-system.

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

Thrust is a powerful library of parallel algorithms and data structures. Thrust provides a flexible, high-level interface for GPU programming that greatly enhances developer productivity. Using Thrust, C++ developers can write just a few lines of code to perform GPU-accelerated sort, scan, transform, and reduction operations orders of magnitude faster than the latest multi-core CPUs.