NVIDIA Software Ecosystem
NGC
NVIDIA NGC™ is the portal of enterprise services, software, management tools, and support for end-to-end AI and digital twin workflows. Bring your solutions to market faster with fully managed services, or take advantage of performance-optimized software to build and deploy solutions on your preferred cloud, on-prem, and edge systems.
NGC Catalog
NGC Catalog is a curated set of GPU-optimized software for AI, HPC and Visualization. The content provided by NVIDIA and third-party ISVs simplifies building, customizing, and integrating GPU-optimized software into workflows, accelerating the time to solutions for users.
nvcr.io
NVIDIA Container Registry (nvcr.io) is a container registry provided by NVIDIA as part of the NVIDIA GPU Cloud (NGC) platform. Contains pre-built, GPU-accelerated Docker containers It’s similar to Docker Hub but tailored for NVIDIA’s GPU-accelerated containers.
Nvidia NIMs (Nvidia Inference Microservices)
Nvidia NIMs (Nvidia inference microservices) are a collection of easy to use API driven microservices to interact with AI models. Explore NIMs
Metropolis NIM Workflows. This repository hosts reference workflows that show how to build applications using NVIDIA Inference Microservices (NIMs). GitHub
NVIDIA VIA (Visual Insight Agent) Microservices are cloud-native building blocks to build AI agents capable of processing large amounts of live or archived videos and images with Vision-Language Models (VLM) - whether deployed at the edge or cloud. This new generation of visual AI agents will help nearly every industry summarize, search, and extract actionable insights from video using natural language. More about VIA
Webinar Build Visual AI Agents With Generative AI and NVIDIA NIM
Use Cases
Can create a workflow for semantic search among large set of images (e.g. “show me all images with a red car”) Example on GitHub
NVDINOv2 (general purpose vision embedding model) Few Shot Classification. By using the NVDINOv2 NIM API, you can quickly generate embeddings on a set of images. These embeddings can stored in a vector database, clustered and searched to build a few shot classification pipeline with no model training or local GPU required. Example on GitHub
Structured text extraction from images (e.g. from ID cards). Example on GitHub
VLM alerts. For example can set an alarm by natural language prompt “is there a fire on the image?” And a camera video stream will be analyzed by the pipeline and if a fire is detected an alarm will be triggered (sent a request by a WebSocket) Example on GitHub
NVIDIA Database/Platform of Use Cases Link
Nvidia Metropolis is a platform that enables the development of AI applications for video analytics. Website
NVIDIA TAO
The open-source NVIDIA TAO (Train, Adapt, Optimize), built on TensorFlow and PyTorch, uses the power of transfer learning while simultaneously simplifying the model training process and optimizing the model for inference throughput on practically any platform. The result is an ultra-streamlined workflow.
Take one of the pretrained models, adapt them to your own real or synthetic data, then optimize for inference throughput. All without needing AI expertise or large training datasets.
NVIDIA AI Foundry
NVIDIA AI Foundry is the end-to-end platform and service for building custom models for generative AI. Just as TSMC manufactures chips designed by other companies, NVIDIA AI Foundry enables organizations to develop their own AI models.
NVIDIA Edify, a multimodal AI architecture that can use simple text prompts to generate images, videos, 3D assets, 360-degree high-dynamic-range imaging and physically based rendering (PBR) materials. Using AI Foundry, companies can train bespoke AI models to generate any of these assets Test Edify models online
NVIDIA Omniverse
NVIDIA Omniverse™ is a platform of APIs, SDKs, and services that enable developers to easily integrate Universal Scene Description (OpenUSD) and NVIDIA RTX™ rendering technologies into existing software tools and simulation workflows for building AI systems.