NVIDIA Documentation Hub

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.

Developer documentation for Megatron Core covers API documentation, quickstart guide as well as deep dives into advanced GPU techniques needed to optimize LLM performance at scale.

NeMo Curator on DGX Cloud provides a cloud-based, GPU-accelerated solution for curating video datasets for post-training. This user guide walks you through the UI and API process for uploading and managing datasets for curation.

NeMo Retriever Extraction (NV-Ingest) is a scalable, performance-oriented document content and metadata extraction microservice. NV-Ingest uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images for use in downstream generative applications.

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 AgentIQ is an open-source library for connecting, evaluating, and accelerating teams of AI agents.

NVIDIA AI Aerial™ is a suite of accelerated computing platforms, software, and services for designing, simulating, and operating wireless networks. Aerial contains hardened RAN software libraries for telcos, cloud service providers (CSPs), and enterprises building commercial 5G networks. Academic and industry researchers can access Aerial on cloud or on-premises setups for advanced wireless and AI/machine learning (ML) research for 6G.

NVIDIA AI Enterprise is an end-to-end, cloud-native software platform that accelerates data science pipelines and streamlines development and deployment of production-grade co-pilots and other generative AI applications. Easy-to-use microservices provide optimized model performance with enterprise-grade security, support, and stability to ensure a smooth transition from prototype to production for enterprises that run their businesses on AI.

A simulation platform that allows users to model data center deployments with full software functionality, creating a digital twin. Transform and streamline network operations by simulating, validating, and automating changes and updates.

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.

Your guide to NVIDIA APIs including NIM and CUDA-X microservices.

The NVIDIA Attestation Suite enhances Confidential Computing by providing robust mechanisms to ensure the integrity and security of devices and platforms. The suite includes NVIDIA Remote Attestation Service (NRAS), the Reference Integrity Manifest (RIM) Service, and the NDIS OCSP Responder.

NVIDIA Base Command Manager streamlines cluster provisioning, workload management, and infrastructure monitoring. It provides all the tools you need to deploy and manage an AI data center. NVIDIA Base Command Manager Essentials comprises the features of NVIDIA Base Command Manager that are certified for use with NVIDIA AI Enterprise.

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

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.

Serverless API to deploy and manage AI workloads on GPUs at planetary scale.

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.

A developer-first world foundation model (WFM) platform designed to help Physical AI developers build their Physical AI systems better and faster.

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) 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, attention, matmul, pooling, and normalization.

NVIDIA cuOpt™ is a GPU-accelerated solver that uses heuristics and metaheuristics to solve complex vehicle routing problem variants with a wide range of constraints.

NVIDIA cuVS is an open-source library for GPU-accelerated vector search and data clustering that enables higher throughput search, lower latency, and faster index build times.

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.

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.

NVIDIA DGX Cloud is an AI platform for enterprise developers, optimized for the demands of generative AI.

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.

Deployment and management guides for NVIDIA DGX SuperPOD, an AI data center infrastructure platform that enables IT to deliver performance—without compromise—for every user and workload. DGX SuperPOD offers leadership-class accelerated infrastructure and agile, scalable performance for the most challenging AI and high-performance computing (HPC) workloads, with industry-proven results.

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.

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.

NVIDIA’s accelerated computing, visualization, and networking solutions are expediting the speed of business outcomes. NVIDIA’s experts are here for you at every step in this fast-paced journey. 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.

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.

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

Find archived online documentation for Riva.

NVIDIA® Riva is an SDK for building multimodal conversational systems. Riva is used for building and deploying AI applications that fuse vision, speech, sensors, and services together to achieve conversational AI use cases that are specific to a domain of expertise. It offers a complete workflow to build, train, and deploy AI systems that can use visual cues such as gestures and gaze along with speech in context.

Instructional Video for AI Enterprise.

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!

Whether you want to see your work across multiple displays or project your ideas in 4K, you can with NVIDIA Mosaic™ multi-display technology. With NVIDIA Mosaic, you can easily span any application across up to 16 high-resolution panels or projectors from a single system, conveniently treating the multiple displays as a single desktop, without application software changes or visual artifacts.

The NVIDIA Collective Communications Library (NCCL) is a library of multi-GPU collective communication primitives that are topology-aware and can be easily integrated into applications. Collective communication algorithms employ many processors working in concert to aggregate data. NCCL is not a full-blown parallel programming framework; rather, it’s a library focused on accelerating collective communication primitives.

The Netherlands Cancer Institute (NKI) has been at the forefront of cancer research and treatment since 1913. Comprised of an internationally acclaimed research center and a dedicated cancer clinic, NKI puts innovative ideas into action for the benefit of patients.

Over the past decade, the rapid development of deep learning convolutional neural networks has completely revolutionized how computer vision tasks are performed. Algorithm, software, and hardware improvements have enabled single computer vision models to run at incredibly fast speeds. This real-time performance opens up new possibilities for a wide range of applications, such as digital surgery.

NVIDIA® Delegated License Service (DLS) is a component of NVIDIA License System that serves licenses to licensed clients. A DLS instance is hosted on-premises at a location that is accessible from your private network, such as inside your data center. The DLS online help is also bundled with the DLS.https://docs.nvidia.com/license-system/dls/index.html

The NVIDIA JetPack SDK, which is the most comprehensive solution for building AI applications, along with L4T and L4T Multimedia, provides the Linux kernel, bootloader, NVIDIA drivers, flashing utilities, sample filesystem, and more for the Jetson platform.

Instructional Video for AI Enterprise.

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 Omniverse is a cloud-native, multi-GPU, real-time simulation and collaboration platform for 3D production pipelines based on Pixar's Universal Scene Description (USD) and NVIDIA RTX.

Previous Release for NVIDIA System Management (NVSM), a software framework for monitoring server nodes, such as NVIDIA DGX servers, in a data center.

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.

Networking management software to reduce latency, increase efficiency, enhance security, and simplify data center automation so applications run faster.

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.

NVIDIA vMaterials are a curated collection of MDL materials and lights representing common real world materials used in design and AEC workflows. Integrating the Iray or MDL SDK quickly brings a library of hundreds of ready to use materials to your application without writing shaders.

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.

In TensorRT, operators represent distinct flavors of mathematical and programmatic operations. The following sections describe every operator that TensorRT supports. The minimum workspace required by TensorRT depends on the operators used by the network. A suggested minimum build-time setting is 16 MB. Regardless of the maximum workspace value provided to the builder, TensorRT will allocate at runtime no more than the workspace it requires.

This NVIDIA TensorRT Developer Guide demonstrates how to use the C++ and Python APIs for implementing the most common deep learning layers. It shows how you can take an existing model built with a deep learning framework and build a TensorRT engine using the provided parsers. The Developer Guide also provides step-by-step instructions for common user tasks such as creating a TensorRT network definition, invoking the TensorRT builder, serializing and deserializing, and how to feed the engine with data and perform inference; all while using either the C++ or Python API.

NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It is designed to work in a complementary fashion with training frameworks such as TensorFlow, PyTorch, and MXNet. It focuses specifically on running an already-trained network quickly and efficiently on NVIDIA hardware.

These support matrices provide a look into the supported platforms, features, and hardware capabilities of the NVIDIA TensorRT APIs, parsers, and layers.

NVIDIA AI Enterprise is certified to deploy on broadly adopted enterprise platforms, including multi-cloud environments, popular data center platforms from VMware and Red Hat, and NVIDIA-Certified Systems.

Receive access to all major releases, feature enhancements, and new hardware support.

NVIDIA System Management is a software framework for monitoring server nodes, such as NVIDIA DGX servers, in a data center.

The nvJPEG Library provides high-performance, GPU-accelerated JPEG encoding and decoding functionality. This library is intended for image formats commonly used in deep learning and hyperscale multimedia applications.

The cuTENSOR library is a first-of-its-kind, GPU-accelerated tensor linear algebra library, providing high-performance tensor contraction, reduction, and element-wise operations. cuTENSOR is used to accelerate applications in the areas of deep learning training and inference, computer vision, quantum chemistry, and computational physics.

Previous Release for NVIDIA System Management (NVSM), a software framework for monitoring server nodes, such as NVIDIA DGX servers, in a data center.

The cuSPARSE library contains a set of basic linear algebra subroutines used for handling sparse matrices. It’s implemented on the NVIDIA CUDA runtime and is designed to be called from C and C++.

Previous Release for NVIDIA System Management (NVSM), a software framework for monitoring server nodes, such as NVIDIA DGX servers, in a data center.

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.

The cuFFT Device Extensions (cuFFTDx) library enables you to perform Fast Fourier Transform (FFT) calculations inside your CUDA kernel. Fusing FFT with other operations can decrease the latency and improve the performance of your application.

With NVIDIA GPU Cloud (NGC) CLI, you can perform many of the same operations that are available from the NGC website, such as running jobs, viewing Docker repositories and downloading AI models within your organization and team space.

Find archived online documentation for CUDA Toolkit. These archives provide access to previously released CUDA documentation versions.

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.

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

This is an entry-level certification that validates foundational concepts of adopting artificial intelligence computing by NVIDIA in a data center environment.

Developer Guide for the latest release of Metropolis Microservices.

Welcome to the NVIDIA Ray Tracing Documentation website where you can explore the latest information about NVIDIA ray tracing software.

End user license agreement for Metropolis Microservices.

Welcome to Isaac ROS, a collection of ROS2 packages for making autonomous robots.

Operate and configure DGX software for Red Hat Enterprise Linux 8 on NVIDIA DGX Systems.

Documentation for users and administrators that explains how to install DGX software on a DGX system installed with CentOS 8 as the base OS.

Previous version of NVTAGS.

Previous version of NVTAGS.

Previous version of NVTAGS.