Posts in category: nVidia AI

Generative AI is unlocking new ways for enterprises to engage customers through digital human avatars.

At SIGGRAPH, NVIDIA previewed “James,” an interactive digital human that can connect with people using emotions, humor and more. James is based on a customer-service workflow using NVIDIA ACE, a reference design for creating custom, hyperrealistic, interactive avatars. Users will soon be able to talk with James in real time at ai.nvidia.com.

NVIDIA also showcased at the computer graphics conference the latest advancements to the NVIDIA Maxine AI platform, including Maxine 3D and Audio2Face-2D for an immersive telepresence experience.

Developers can use Maxine and NVIDIA ACE digital human technologies to make customer interactions with digital interfaces more engaging and natural. ACE technologies enable digital human development with AI models for speech and translation, vision, intelligence, lifelike animation and behavior, and realistic appearance.

Companies across industries are using Maxine and ACE to deliver immersive virtual customer experiences.

Meet James, a Digital Brand Ambassador

Built on top of NVIDIA NIM microservices, James is a virtual assistant that can provide contextually accurate responses.

Using retrieval-augmented generation (RAG), James can accurately tell users about the latest NVIDIA technologies. ACE allows developers to use their own data to create domain-specific avatars that can communicate relevant information to customers.

James is powered by the latest NVIDIA RTX rendering technologies for advanced, lifelike animations. His natural-sounding voice is powered by ElevenLabs. NVIDIA ACE lets developers customize animation, voice and language when building avatars tailored for different use cases.

NVIDIA Maxine Enhances Digital Humans in Telepresence

Maxine, a platform for deploying cutting-edge AI features that enhance the audio and video quality of digital humans, enables the use of real-time, photorealistic 2D and 3D avatars with video-conferencing devices.

Maxine 3D converts 2D video portrait inputs into 3D avatars, allowing the integration of highly realistic digital humans in video conferencing and other two-way communication applications. The technology will soon be available in early access.

Audio2Face-2D, currently in early access, animates static portraits based on audio input, creating dynamic, speaking digital humans from a single image. Try the technology at ai.nvidia.com.

Companies Embracing Digital Human Applications

HTC, Looking Glass, Reply and UneeQ are among the latest companies using NVIDIA ACE and Maxine across a broad range of use cases, including customer service agents, and telepresence experiences in entertainment, retail and hospitality.

At SIGGRAPH, digital human technology developer UneeQ is showcasing two new demos.

The first spotlights cloud-rendered digital humans powered by NVIDIA GPUs with local, in-browser computer vision for enhanced scalability and privacy, and animated using the Audio2Face-3D NVIDIA NIM microservice. UneeQ’s Synapse technology processes anonymized user data and feeds it to a large language model (LLM) for more accurate, responsive interactions.

The second demo runs on a single NVIDIA RTX GPU-powered laptop, featuring an advanced digital human powered by Gemma 7B LLM, RAG and the NVIDIA Audio2Face-3D NIM microservice.

Both demos showcase UneeQ’s NVIDIA-powered efforts to develop digital humans that can react to users’ facial expressions and actions, pushing the boundaries of realism in virtual customer service experiences.

HTC Viverse has integrated the Audio2Face-3D NVIDIA NIM microservice into its VIVERSE AI agent for dynamic facial animation and lip sync, allowing for more natural and immersive user interactions.

Hologram technology company Looking Glass’ Magic Mirror demo at SIGGRAPH uses a simple camera setup and Maxine’s advanced 3D AI capabilities to generate a real-time holographic feed of users’ faces on its newly launched, group-viewable Looking Glass 16-inch and 32-inch Spatial Displays.

Reply is unveiling an enhanced version of Futura, its cutting-edge digital human developed for Costa Crociere’s Costa Smeralda cruise ship. Powered by Audio2Face-3D NVIDIA NIM and Riva ASR NIM microservices, Futura’s speech-synthesis capabilities tap advanced technologies including GPT-4o, LlamaIndex for RAG and Microsoft Azure text-to-speech services.

Futura also incorporates Reply’s proprietary affective computing technology, alongside Hume AI and MorphCast, for comprehensive emotion recognition. Built using Unreal Engine 5.4.3 and MetaHuman Creator with NVIDIA ACE-powered facial animation, Futura supports six languages. The intelligent assistant can help plan personalized port visits, suggest tailored itineraries and facilitate tour bookings.

In addition, Futura refines recommendations based on guest feedback and uses a specially created knowledge base to provide informative city presentations, enhancing tourist itineraries. Futura aims to enhance customer service and offer immersive interactions in real-world scenarios, leading to streamlined operations and driving business growth.

Learn more about NVIDIA ACE and NVIDIA Maxine. 

Discover how accelerated computing and generative AI are transforming industries and creating new opportunities for innovation by watching NVIDIA founder and CEO Jensen Huang’s fireside chats at SIGGRAPH.

See notice regarding software product information.

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Millions of people already use generative AI to assist in writing and learning. Now, the technology can also help them more effectively navigate the physical world.

NVIDIA announced at SIGGRAPH generative physical AI advancements including the NVIDIA Metropolis reference workflow for building interactive visual AI agents and new NVIDIA NIM microservices that will help developers train physical machines and improve how they handle complex tasks.

These include three fVDB NIM microservices that support NVIDIA’s new deep learning framework for 3D worlds, as well as the USD Code, USD Search and USD Validate NIM microservices for working with Universal Scene Description (aka OpenUSD).

The NVIDIA OpenUSD NIM microservices work together with the world’s first generative AI models for OpenUSD development — also developed by NVIDIA — to enable developers to incorporate generative AI copilots and agents into USD workflows and broaden the possibilities of 3D worlds.

NVIDIA NIM Microservices Transform Physical AI Landscapes

Physical AI uses advanced simulations and learning methods to help robots and other industrial automation more effectively perceive, reason and navigate their surroundings. The technology is transforming industries like manufacturing and healthcare, and advancing smart spaces with robots, factory and warehouse technologies, surgical AI agents and cars that can operate more autonomously and precisely.

NVIDIA offers a broad range of NIM microservices customized for specific models and industry domains. NVIDIA’s suite of NIM microservices tailored for physical AI supports capabilities for speech and translation, vision and intelligence, and realistic animation and behavior.

Turning Visual AI Agents Into Visionaries With NVIDIA NIM

Visual AI agents use computer vision capabilities to perceive and interact with the physical world and perform reasoning tasks.

Highly perceptive and interactive visual AI agents are powered by a new class of generative AI models called vision language models (VLMs), which bridge digital perception and real-world interaction in physical AI workloads to enable enhanced decision-making, accuracy, interactivity and performance. With VLMs, developers can build vision AI agents that can more effectively handle challenging tasks, even in complex environments.

Generative AI-powered visual AI agents are rapidly being deployed across hospitals, factories, warehouses, retail stores, airports, traffic intersections and more.

To help physical AI developers more easily build high-performing, custom visual AI agents, NVIDIA offers NIM microservices and reference workflows for physical AI. The NVIDIA Metropolis reference workflow provides a simple, structured approach for customizing, building and deploying visual AI agents, as detailed in the blog.

NVIDIA NIM Helps K2K Make Palermo More Efficient, Safe and Secure

City traffic managers in Palermo, Italy, deployed visual AI agents using NVIDIA NIM to uncover physical insights that help them better manage roadways.

K2K, an NVIDIA Metropolis partner, is leading the effort, integrating NVIDIA NIM microservices and VLMs into AI agents that analyze the city’s live traffic cameras in real time. City officials can ask the agents questions in natural language and receive fast, accurate insights on street activity and suggestions on how to improve the city’s operations, like adjusting traffic light timing.

Leading global electronics giants Foxconn and Pegatron have adopted physical AI, NIM microservices and Metropolis reference workflows to more efficiently design and run their massive manufacturing operations.

The companies are building virtual factories in simulation to save significant time and costs. They’re also running more thorough tests and refinements for their physical AI — including AI multi-camera and visual AI agents — in digital twins before real-world deployment, improving worker safety and leading to operational efficiencies.

Bridging the Simulation-to-Reality Gap With Synthetic Data Generation

Many AI-driven businesses are now adopting a “simulation-first” approach for generative physical AI projects involving real-world industrial automation.

Manufacturing, factory logistics and robotics companies need to manage intricate human-worker interactions, advanced facilities and expensive equipment. NVIDIA physical AI software, tools and platforms — including physical AI and VLM NIM microservices, reference workflows and fVDB — can help them streamline the highly complex engineering required to create digital representations or virtual environments that accurately mimic real-world conditions.

VLMs are seeing widespread adoption across industries because of their ability to generate highly realistic imagery. However, these models can be challenging to train because of the immense volume of data required to create an accurate physical AI model.

Synthetic data generated from digital twins using computer simulations offers a powerful alternative to real-world datasets, which can be expensive — and sometimes impossible — to acquire for model training, depending on the use case.

Tools like NVIDIA NIM microservices and Omniverse Replicator let developers build generative AI-enabled synthetic data pipelines to accelerate the creation of robust, diverse datasets for training physical AI. This enhances the adaptability and performance of models such as VLMs, enabling them to generalize more effectively across industries and use cases.

Availability

Developers can access state-of-the-art, open and NVIDIA-built foundation AI models and NIM microservices at ai.nvidia.com. The Metropolis NIM reference workflow is available in the GitHub repository, and Metropolis VIA microservices are available for download in developer preview.

OpenUSD NIM microservices are available in preview through the NVIDIA API catalog.

Watch how accelerated computing and generative AI are transforming industries and creating new opportunities for innovation and growth in NVIDIA founder and CEO Jensen Huang’s fireside chats at SIGGRAPH.

See notice regarding software product information.

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Designers and artists have new and improved ways to boost their productivity with generative AI trained on licensed data.

Shutterstock, a leading platform for creative content, launched its Generative 3D service in commercial beta. It lets creators quickly prototype 3D assets and generate 360 HDRi backgrounds that light scenes, using just text or image prompts.

Getty Images, a premier visual content creator and marketplace, turbocharged its Generative AI by Getty Images service so it creates images twice as fast, improves output quality, brings advanced controls and enables fine-tuning.

The services are built with NVIDIA’s visual AI foundry using NVIDIA Edify, a multimodal generative AI architecture. The AI models are then optimized and packaged for maximum performance with NVIDIA NIM, a set of accelerated microservices for AI inference.

Edify enables service providers to train responsible generative models on their licensed data and scale them quickly with NVIDIA DGX Cloud, the cloud-first way to get the best of NVIDIA AI.

Generative AI Speeds 3D Modeling

Available now for enterprises in commercial beta, Shutterstock’s service lets designers and artists quickly create 3D objects that help them prototype or populate virtual environments. For example, tapping generative AI, they can quickly create the silverware and plates on a dining room table so they can focus on designing the characters around it.

The 3D assets the service generates are ready to edit using digital content creation tools, and available in a variety of popular file formats. Their clean geometry and layout gives artists an advanced starting point for adding their own flair.

The AI model first delivers a preview of a single asset in as little as 10 seconds. If users like it, the preview can be turned into a higher-quality 3D asset, complete with physically based rendering materials like concrete, wood or leather.

At this year’s SIGGRAPH computer graphics conference, designers will see just how fast they can make their ideas come to life.

Shutterstock will demo a workflow in Blender that lets artists generate objects directly within their 3D environment. In the Shutterstock booth at SIGGRAPH, HP will show 3D prints and physical prototypes of the kinds of assets attendees can design on the show floor using Generative 3D.

Shutterstock is also working with global marketing and communications services company WPP to bring ideas to life with Edify 3D generation for virtual production (see video below).

Explore Generative 3D by Shutterstock on the company’s website, or test-drive the application programming interface (API) at build.nvidia.com/.

Virtual Lighting Gets Real

Lighting a virtual scene with accurate reflections can be a complicated task. Creatives need to operate expensive 360-degree camera rigs and go on set to create backgrounds from scratch, or search vast libraries for something that approximates what they want.

With Shutterstock’s Generative 3D service, users can now simply describe the exact environment they need in text or with an image, and out comes a high-dynamic-range panoramic image, aka 360 HDRi, in brilliant 16K resolution. (See video below.)

Want that beautiful new sports car shown in a desert, a tropical beach or maybe on a winding mountain road? With generative AI, designers can shift gears fast.

Three companies plan to integrate Shutterstock’s 360 HDRi APIs directly into their workflows — WPP, CGI studio Katana and Dassault Systèmes, developer of the 3DEXCITE applications for creating high-end visualizations and 3D content for virtual worlds.

Great Images Get a Custom Fit

Generative AI by Getty Images has upgraded to a more powerful Edify AI model with a portfolio of new features that let artists control image composition and style.

Want a red beach ball floating above that perfect shot of a coral reef in Fiji? Getty Images’ service can get it done in a snap.

The new model is twice as fast, boosts image quality and prompt accuracy, and lets users control camera settings like the depth of field or focal length of a shot. Users can generate four images in about six seconds and scale them up to 4K resolution.

In addition, the commercially safe foundational model now serves as the basis for a fine-tuning capability that lets companies customize the AI with their own data. That lets them generate images tailored to the creative style of their specific brands.

New controls in the service support the use of a sketch or depth map to guide the composition or structure of an image.

Creatives at Omnicom, a global leader in marketing and sales solutions, are using Getty Images’ service to streamline advertising workflows and safely create on-brand content. The collaboration with Getty Images is part of Omnicom’s strategy to infuse generative AI into every facet of its business, helping teams move from ideas to outcomes faster.

Generative AI by Getty Images is available through the Getty Images and iStock websites, and via an API.

For more about NVIDIA’s offerings, read about the AI foundry for visual generative AI built on NVIDIA DGX Cloud, and try it on ai.nvidia.com.

To get the big picture, listen to NVIDIA founder and CEO Jensen Huang in two fireside chats at SIGGRAPH.

See notice regarding software product information.

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“Hey, you. You’re finally awake.”

It’s the summer of Elder Scrolls — whether a seasoned Dragonborn or a new adventurer, dive into the legendary world of Tamriel this GFN Thursday as The Elder Scrolls V: Skyrim Special Edition joins the cloud.

Epic adventures await, along with nine new games joining the GeForce NOW library this week.

Plus make sure to catch the GeForce NOW Summer Sale for 50% off new Ultimate and Priority memberships.

Unleash the Dragonborn

Experience the legendary adventures, breathtaking landscapes and immersive storytelling of the iconic role-playing game The Elder Scrolls V: Skyrim Special Edition from Bethesda Game Studios — now accessible on any device from the cloud. Become the Dragonborn and defeat Alduin the World-Eater, a dragon prophesied to destroy the world. 

Explore a vast landscape, complete quests and improve skills to develop characters in the open world of Skyrim. The Special Edition includes add-ons with all-new features, including remastered art and effects. It also brings the adventure of Bethesda Game Studios creations, including new quests, environments, characters, dialogue, armor and weapons.

Get ready to embark on unforgettable quests, battle fearsome foes and uncover the rich lore of the Elder Scrolls universe, all with the power and convenience of GeForce NOW. “Fus Ro Dah” with an Ultimate membership to stream at up to 4K resolution and 120 frames per second with up to eight-hour gaming sessions for the ultimate immersive experience throughout the realms of Tamriel.

All Hands on Deck

Wargaming is bringing back an in-game event exclusively for GeForce NOW members this week.

Through Tuesday, July 30, members who complete the quest while streaming World of Warships can earn up to five GeForce NOW one-day Priority codes — one for each day of the challenge. Aspiring admirals can learn more on the World of Warships blog and social channels.

Shiny and New

Take on classic survival horror in CONSCRIPT from Jordan Mochi and Team17. Inspired by legendary games in the genre, the game is set in 1916 during the Great War. CONSCRIPT blends all the punishing mechanics of older horror games into a cohesive, tense and unique experience. Play as a French soldier searching for his missing-in-action brother during the Battle of Verdun. Search through twisted trenches, navigate overrun forts and cross no-man’s-land to find him.

Here’s the full list of new games this week:

• Cataclismo (New release on Steam, July 22
• CONSCRIPT (New release on Steam, July 23)
• F1 Manager 2024 (New release on Steam, July 23)
• EARTH DEFENSE FORCE 6 (New release on Steam, July 25)
• The Elder Scrolls V: Skyrim (Steam)
• The Elder Scrolls V: Skyrim Special Edition (Steam, Epic Games Store and Xbox, available on PC Game Pass)
• Gang Beasts (Steam and Xbox, available on PC Game Pass)
• Kingdoms and Castles (Steam)
• The Settlers: New Allies (Steam)

What are you planning to play this weekend? Let us know on X or in the comments below.

ʏᴏᴜʀ ᴄʟᴏᴜᴅ ɢᴀᴍɪɴɢ ꜱᴋɪʟʟ ɪɴᴄʀᴇᴀꜱᴇᴅ

— NVIDIA GeForce NOW (@NVIDIAGFN) July 24, 2024

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Editor’s note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for RTX PC users.

Adobe Creative Cloud applications, which tap NVIDIA RTX GPUs, are designed to enhance the creativity of users, empowering them to work faster and focus on their craft.

These tools seamlessly integrate into existing creator workflows, enabling greater productivity and delivering power and precision.

Look to the Light

Generative AI creates new data in forms such as images or text by learning from existing data. It effectively visualizes and generates content to match what a user describes and helps open up fresh avenues for creativity.

Adobe Firefly is Adobe’s family of creative generative AI models that offer new ways to ideate and create while assisting creative workflows using generative AI. They’re designed to be safe for commercial use and were trained, using NVIDIA GPUs, on licensed content, like Adobe Stock Images, and public domain content where copyright has expired.

Firefly features are integrated in Adobe’s most popular creative apps.

Adobe Photoshop features the Generative Fill tool, which uses simple description prompts to easily add content from images. With the latest Reference Image feature currently in beta, users can also upload a sample image to get image results closer to their desired output.

Generative Expand allows artists to extend the border of their image with the Crop tool, filling in bigger canvases with new content that automatically blends in with the existing image.

RTX-accelerated Neural Filters, such as Photo Restoration, enable complex adjustments such as colorizing black-and-white photos and performing style transfers using AI. The Smart Portrait filter, which allows non-destructive editing with filters, is based on work from NVIDIA Research.

The brand-new Generative Shape Fill (beta) in Adobe Illustrator, powered by the latest Adobe Firefly Vector Model, allows users to accelerate design workflows by quickly filling shapes with detail and color in their own styles. With Generative Shape Fill, designers can easily match the style and color of their own artwork to create a wide variety of editable and scalable vector graphic options.

Adobe Illustrator’s Generative Recolor feature lets creators type in a text prompt to explore custom color palettes and themes for their vector artwork in seconds.

NVIDIA will continue working with Adobe to support advanced generative AI models, with a focus on deep integration into the apps the world’s leading creators use.

Making Moves on Video

Adobe Premiere Pro is one of the most popular and powerful video editing solutions.

Its Enhance Speech tool, accelerated by RTX, uses AI to remove unwanted noise and improve the quality of dialogue clips so they sound professionally recorded. It’s up to 4.5x faster on RTX PCs.

Auto Reframe, another Adobe Premiere feature, uses GPU acceleration to identify and track the most relevant elements in a video, and intelligently reframes video content for different aspect ratios. Scene Edit Detection automatically finds the original edit points in a video, a necessary step before the video editing stage begins.

Visual Effects

Separating a foreground object from a background is a crucial step in many visual effects and compositing workflows.

Adobe After Effects has a new feature that uses a matte to isolate an object, enabling capabilities including background replacement and the selective application of effects to the foreground.

Using the Roto Brush tool, artists can draw strokes on representative areas of the foreground and background elements. After Effects uses that information to create a segmentation boundary between the foreground and background elements, delivering cleaner cutouts with fewer clicks.

Creating 3D Product Shots

The Substance 3D Collection is Adobe’s solution for 3D material authoring, texturing and rendering, enabling users to rapidly create stunningly photorealistic 3D content, including models, materials and lighting.

Visualizing products and designs in the context of a space is compelling, but it can be time-consuming to find the right environment for the objects to live in. Substance 3D Stager’s Generative Background feature, powered by Adobe Firefly, solves this issue by letting artists quickly explore generated backgrounds to composite 3D models.

Once an environment is selected, Stager can automatically match the perspective and lighting to the generated background.

Material Authoring With AI

Adobe Substance 3D Sampler, also part of the Substance 3D Collection, is designed to transform images of surfaces and objects into photorealistic physically based rendering (PBR) materials, 3D models and high-dynamic range environment lights. With the recent introduction of new generative workflows powered by Adobe Firefly, Sampler is making it easier than ever for artists to explore variations when creating materials for everything from product visualization projects to the latest AAA games.

Sampler’s Text-to-Texture feature allows users to generate tiled images from detailed text prompts. These generated images can then be edited and transformed into photorealistic PBR materials using the machine learning-powered Image-to-Material feature or any Sampler filter.

Image-to-Texture similarly enables the creation of tiled textures from reference images, providing an alternate way to prompt and generate variations from existing visual content.

Sampler’s Text-to-Pattern feature uses text prompts to generate tiling patterns, which can be used as base colors or inputs for various filters, such as the Cloth Weave filter for creating original fabric materials.

All of these generative AI features in the Substance 3D Collection, supercharged with RTX GPUs, are designed to help 3D creators ideate and create faster.

Photo-tastic Features

Adobe Lightroom’s AI-powered Raw Details feature produces crisp detail and more accurate renditions of edges, improves color rendering and reduces artifacts, enhancing the image without changing its original resolution. This feature is handy for large displays and prints, where fine details are visible.

Super Resolution helps create an enhanced image with similar results as Raw Details but with 2x the linear resolution. This means that the enhanced image will have 2x the width and height of the original image — or 4x the total pixel count. This is especially useful for increasing the resolution of cropped imagery.

For faster editing, AI-powered, RTX-accelerated masking tools like Select Subject, which isolates people from an image, and Select Sky, which captures skies, enable users to create complex masks with the click of a button.

Visit Adobe’s AI features page for a complete list of AI features using RTX.

Looking for more AI-powered content creation apps? Consider NVIDIA Broadcast, which transforms any room into a home studio, free for RTX GPU owners. 

Generative AI is transforming gaming, videoconferencing and interactive experiences of all kinds. Make sense of what’s new and what’s next by subscribing to the AI Decoded newsletter.

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AI is set to transform the workforce — and the Georgia Institute of Technology’s new AI Makerspace is helping tens of thousands of students get ahead of the curve. In this episode of NVIDIA’s AI Podcast, host Noah Kravitz speaks with Arijit Raychowdhury, a professor and Steve W. Cedex school chair of electrical engineering at Georgia Tech’s college of engineering, about the supercomputer hub, which provides students with the computing resources to reinforce their coursework and gain hands-on experience with AI. Built in collaboration with NVIDIA, the AI Makerspace underscores Georgia Tech’s commitment to preparing students for an AI-driven future, while fostering collaboration with local schools and universities.

Time Stamps

1:45: What is the AI Makerspace?

5:57: What computing resources are included in the AI Makerspace?

7:23: What is the aim of the AI Makerspace?

14:47: Georgia Tech’s AI-focused minor and coursework

19:25: Raychowdhury’s insight on the intersection of AI and higher education

23:33: How have industries and jobs already changed as a result of AI?

27:44: What can younger students do to prepare to get a spot in Georgia Tech’s engineering program?

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Businesses seeking to harness the power of AI need customized models tailored to their specific industry needs.

NVIDIA AI Foundry is a service that enables enterprises to use data, accelerated computing and software tools to create and deploy custom models that can supercharge their generative AI initiatives.

Just as TSMC manufactures chips designed by other companies, NVIDIA AI Foundry provides the infrastructure and tools for other companies to develop and customize AI models — using DGX Cloud, foundation models, NVIDIA NeMo software, NVIDIA expertise, as well as ecosystem tools and support.

The key difference is the product: TSMC produces physical semiconductor chips, while NVIDIA AI Foundry helps create custom models. Both enable innovation and connect to a vast ecosystem of tools and partners.

Enterprises can use AI Foundry to customize NVIDIA and open community models, including the new Llama 3.1 collection, as well as NVIDIA Nemotron, CodeGemma by Google DeepMind, CodeLlama, Gemma by Google DeepMind, Mistral, Mixtral, Phi-3, StarCoder2 and others.

Industry Pioneers Drive AI Innovation

Industry leaders Amdocs, Capital One, Getty Images, KT, Hyundai Motor Company, SAP, ServiceNow and Snowflake are among the first using NVIDIA AI Foundry. These pioneers are setting the stage for a new era of AI-driven innovation in enterprise software, technology, communications and media.

“Organizations deploying AI can gain a competitive edge with custom models that incorporate industry and business knowledge,” said Jeremy Barnes, vice president of AI Product at ServiceNow. “ServiceNow is using NVIDIA AI Foundry to fine-tune and deploy models that can integrate easily within customers’ existing workflows.”

The Pillars of NVIDIA AI Foundry 

NVIDIA AI Foundry is supported by the key pillars of foundation models, enterprise software, accelerated computing, expert support and a broad partner ecosystem.

Its software includes AI foundation models from NVIDIA and the AI community as well as the complete NVIDIA NeMo software platform for fast-tracking model development.

The computing muscle of NVIDIA AI Foundry is NVIDIA DGX Cloud, a network of accelerated compute resources co-engineered with the world’s leading public clouds — Amazon Web Services, Google Cloud and Oracle Cloud Infrastructure. With DGX Cloud, AI Foundry customers can develop and fine-tune custom generative AI applications with unprecedented ease and efficiency, and scale their AI initiatives as needed without significant upfront investments in hardware. This flexibility is crucial for businesses looking to stay agile in a rapidly changing market.

If an NVIDIA AI Foundry customer needs assistance, NVIDIA AI Enterprise experts are on hand to help. NVIDIA experts can walk customers through each of the steps required to build, fine-tune and deploy their models with proprietary data, ensuring the models tightly align with their business requirements.

NVIDIA AI Foundry customers have access to a global ecosystem of partners that can provide a full range of support. Accenture, Deloitte, Infosys and Wipro are among the NVIDIA partners that offer AI Foundry consulting services that encompass design, implementation and management of AI-driven digital transformation projects. Accenture is first to offer its own AI Foundry-based offering for custom model development, the Accenture AI Refinery framework.

Additionally, service delivery partners such as Data Monsters, Quantiphi, Slalom and SoftServe help enterprises navigate the complexities of integrating AI into their existing IT landscapes, ensuring that AI applications are scalable, secure and aligned with business objectives.

Customers can develop NVIDIA AI Foundry models for production using AIOps and MLOps platforms from NVIDIA partners, including Cleanlab, DataDog, Dataiku, Dataloop, DataRobot, Domino Data Lab, Fiddler AI, New Relic, Scale and Weights & Biases.

Customers can output their AI Foundry models as NVIDIA NIM inference microservices — which include the custom model, optimized engines and a standard API — to run on their preferred accelerated infrastructure.

Inferencing solutions like NVIDIA TensorRT-LLM deliver improved efficiency for Llama 3.1 models to minimize latency and maximize throughput. This enables enterprises to generate tokens faster while reducing total cost of running the models in production. Enterprise-grade support and security is provided by the NVIDIA AI Enterprise software suite.

The broad range of deployment options includes NVIDIA-Certified Systems from global server manufacturing partners including Cisco, Dell Technologies, Hewlett Packard Enterprise, Lenovo and Supermicro, as well as cloud instances from Amazon Web Services, Google Cloud and Oracle Cloud Infrastructure.

Additionally, Together AI, a leading AI acceleration cloud, today announced it will enable its ecosystem of over 100,000 developers and enterprises to use its NVIDIA GPU-accelerated inference stack to deploy Llama 3.1 endpoints and other open models on DGX Cloud.

“Every enterprise running generative AI applications wants a faster user experience, with greater efficiency and lower cost,” said Vipul Ved Prakash, founder and CEO of Together AI. “Now, developers and enterprises using the Together Inference Engine can maximize performance, scalability and security on NVIDIA DGX Cloud.”

NVIDIA NeMo Speeds and Simplifies Custom Model Development

With NVIDIA NeMo integrated into AI Foundry, developers have at their fingertips the tools needed to curate data, customize foundation models and evaluate performance. NeMo technologies include:

• NeMo Curator is a GPU-accelerated data-curation library that improves generative AI model performance by preparing large-scale, high-quality datasets for pretraining and fine-tuning.
• NeMo Customizer is a high-performance, scalable microservice that simplifies fine-tuning and alignment of LLMs for domain-specific use cases.
• NeMo Evaluator provides automatic assessment of generative AI models across academic and custom benchmarks on any accelerated cloud or data center.
• NeMo Guardrails orchestrates dialog management, supporting accuracy, appropriateness and security in smart applications with large language models to provide safeguards for generative AI applications.

Using the NeMo platform in NVIDIA AI Foundry, businesses can create custom AI models that are precisely tailored to their needs. This customization allows for better alignment with strategic objectives, improved accuracy in decision-making and enhanced operational efficiency. For instance, companies can develop models that understand industry-specific jargon, comply with regulatory requirements and integrate seamlessly with existing workflows.

“As a next step of our partnership, SAP plans to use NVIDIA’s NeMo platform to help businesses to accelerate AI-driven productivity powered by SAP Business AI,” said Philipp Herzig, chief AI officer at SAP.

Enterprises can deploy their custom AI models in production with NVIDIA NeMo Retriever NIM inference microservices. These help developers fetch proprietary data to generate knowledgeable responses for their AI applications with retrieval-augmented generation (RAG).

“Safe, trustworthy AI is a non-negotiable for enterprises harnessing generative AI, with retrieval accuracy directly impacting the relevance and quality of generated responses in RAG systems,” said Baris Gultekin, Head of AI, Snowflake. “Snowflake Cortex AI leverages NeMo Retriever, a component of NVIDIA AI Foundry, to further provide enterprises with easy, efficient, and trusted answers using their custom data.”

Custom Models Drive Competitive Advantage

One of the key advantages of NVIDIA AI Foundry is its ability to address the unique challenges faced by enterprises in adopting AI. Generic AI models can fall short of meeting specific business needs and data security requirements. Custom AI models, on the other hand, offer superior flexibility, adaptability and performance, making them ideal for enterprises seeking to gain a competitive edge.

Learn more about how NVIDIA AI Foundry allows enterprises to boost productivity and innovation.

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Generative AI applications have little, or sometimes negative, value without accuracy — and accuracy is rooted in data.

To help developers efficiently fetch the best proprietary data to generate knowledgeable responses for their AI applications, NVIDIA today announced four new NVIDIA NeMo Retriever NIM inference microservices.

Combined with NVIDIA NIM inference microservices for the Llama 3.1 model collection, also announced today, NeMo Retriever NIM microservices enable enterprises to scale to agentic AI workflows — where AI applications operate accurately with minimal intervention or supervision — while delivering the highest accuracy retrieval-augmented generation, or RAG.

NeMo Retriever allows organizations to seamlessly connect custom models to diverse business data and deliver highly accurate responses for AI applications using RAG. In essence, the production-ready microservices enable highly accurate information retrieval for building highly accurate AI applications.

For example, NeMo Retriever can boost model accuracy and throughput for developers creating AI agents and customer service chatbots, analyzing security vulnerabilities or extracting insights from complex supply chain information.

NIM inference microservices enable high-performance, easy-to-use, enterprise-grade inferencing. And with NeMo Retriever NIM microservices, developers can benefit from all of this — superpowered by their data.

These new NeMo Retriever embedding and reranking NIM microservices are now generally available:

• NV-EmbedQA-E5-v5, a popular community base embedding model optimized for text question-answering retrieval
• NV-EmbedQA-Mistral7B-v2, a popular multilingual community base model fine-tuned for text embedding for high-accuracy question answering
• Snowflake-Arctic-Embed-L, an optimized community model, and
• NV-RerankQA-Mistral4B-v3, a popular community base model fine-tuned for text reranking for high-accuracy question answering.

They join the collection of NIM microservices easily accessible through the NVIDIA API catalog.

Embedding and Reranking Models

NeMo Retriever NIM microservices comprise two model types — embedding and reranking — with open and commercial offerings that ensure transparency and reliability.

An embedding model transforms diverse data — such as text, images, charts and video — into numerical vectors, stored in a vector database, while capturing their meaning and nuance. Embedding models are fast and computationally less expensive than traditional large language models, or LLMs.

A reranking model ingests data and a query, then scores the data according to its relevance to the query. Such models offer significant accuracy improvements while being computationally complex and slower than embedding models.

NeMo Retriever provides the best of both worlds. By casting a wide net of data to be retrieved with an embedding NIM, then using a reranking NIM to trim the results for relevancy, developers tapping NeMo Retriever can build a pipeline that ensures the most helpful, accurate results for their enterprise.

With NeMo Retriever, developers get access to state-of-the-art open, commercial models for building text Q&A retrieval pipelines that provide the highest accuracy. When compared with alternate models, NeMo Retriever NIM microservices provided 30% fewer inaccurate answers for enterprise question answering.

Top Use Cases

From RAG and AI agent solutions to data-driven analytics and more, NeMo Retriever powers a wide range of AI applications.

The microservices can be used to build intelligent chatbots that provide accurate, context-aware responses. They can help analyze vast amounts of data to identify security vulnerabilities. They can assist in extracting insights from complex supply chain information. And they can boost AI-enabled retail shopping advisors that offer natural, personalized shopping experiences, among other tasks.

NVIDIA AI workflows for these use cases provide an easy, supported starting point for developing generative AI-powered technologies.

Dozens of NVIDIA data platform partners are working with NeMo Retriever NIM microservices to boost their AI models’ accuracy and throughput.

DataStax has integrated NeMo Retriever embedding NIM microservices in its Astra DB and Hyper-Converged platforms, enabling the company to bring accurate, generative AI-enhanced RAG capabilities to customers with faster time to market.

Cohesity will integrate NVIDIA NeMo Retriever microservices with its AI product, Cohesity Gaia, to help customers put their data to work to power insightful, transformative generative AI applications through RAG.

Kinetica will use NVIDIA NeMo Retriever to develop LLM agents that can interact with complex networks in natural language to respond more quickly to outages or breaches — turning insights into immediate action.

NetApp is collaborating with NVIDIA to connect NeMo Retriever microservices to exabytes of data on its intelligent data infrastructure. Every NetApp ONTAP customer will be able to seamlessly “talk to their data” to access proprietary business insights without having to compromise the security or privacy of their data.

NVIDIA global system integrator partners including Accenture, Deloitte, Infosys, LTTS, Tata Consultancy Services, Tech Mahindra and Wipro, as well as service delivery partners Data Monsters, EXLService (Ireland) Limited, Latentview, Quantiphi, Slalom, SoftServe and Tredence, are developing services to help enterprises add NeMo Retriever NIM microservices into their AI pipelines.

Use With Other NIM Microservices

NeMo Retriever NIM microservices can be used with NVIDIA Riva NIM microservices, which  supercharge speech AI applications across industries — enhancing customer service and enlivening digital humans.

New models that will soon be available as Riva NIM microservices include: FastPitch and HiFi-GAN for text-to-speech applications; Megatron for multilingual neural machine translation; and the record-breaking NVIDIA Parakeet family of models for automatic speech recognition.

NVIDIA NIM microservices can be used all together or separately, offering developers a modular approach to building AI applications. In addition, the microservices can be integrated with community models, NVIDIA models or users’ custom models — in the cloud, on premises or in hybrid environments — providing developers with further flexibility.

NVIDIA NIM microservices are available at ai.nvidia.com. Enterprises can deploy AI applications in production with NIM through the NVIDIA AI Enterprise software platform.

NIM microservices can run on customers’ preferred accelerated infrastructure, including cloud instances from Amazon Web Services, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure, as well as NVIDIA-Certified Systems from global server manufacturing partners including Cisco, Dell Technologies, Hewlett Packard Enterprise, Lenovo and Supermicro.

NVIDIA Developer Program members will soon be able to access NIM for free for research, development and testing on their preferred infrastructure.

Learn more about the latest in generative AI and accelerated computing by joining NVIDIA at SIGGRAPH, the premier computer graphics conference, running July 28-Aug. 1 in Denver. 

See notice regarding software product information.

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Team NVIDIA has triumphed at the Amazon KDD Cup 2024, securing first place Friday across all five competition tracks.

The team — consisting of NVIDIANs Ahmet Erdem, Benedikt Schifferer, Chris Deotte, Gilberto Titericz, Ivan Sorokin and Simon Jegou — demonstrated its prowess in generative AI, winning in categories that included text generation, multiple-choice questions, name entity recognition, ranking, and retrieval.

The competition, themed “Multi-Task Online Shopping Challenge for LLMs,” asked participants to solve various challenges using limited datasets.

“The new trend in LLM competitions is that they don’t give you training data,” said Deotte, a senior data scientist at NVIDIA. “They give you 96 example questions — not enough to train a model — so we came up with 500,000 questions on our own.”

Deotte explained that the NVIDIA team generated a variety of questions by writing some themselves, using a large language model to create others, and transforming existing e-commerce datasets.

“Once we had our questions, it was straightforward to use existing frameworks to fine-tune a language model,” he said.

The competition organizers hid the test questions to ensure participants couldn’t exploit previously known answers. This approach encourages models that generalize well to any question about e-commerce, proving the model’s capability to handle real-world scenarios effectively.

Despite these constraints, Team NVIDIA’s innovative approach outperformed all competitors by using Qwen2-72B, a just-released LLM with 72 billion parameters, fine-tuned on eight NVIDIA A100 Tensor Core GPUs, and employing QLoRA, a technique for fine-tuning models with datasets.

About the KDD Cup 2024

The KDD Cup, organized by the Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining, or ACM SIGKDD, is a prestigious annual competition that promotes research and development in the field.

This year’s challenge, hosted by Amazon, focused on mimicking the complexities of online shopping with the goal of making it a more intuitive and satisfying experience using large language models. Organizers utilized the test dataset ShopBench — a benchmark that replicates the massive challenge for online shopping with 57 tasks and about 20,000 questions derived from real-world Amazon shopping data — to evaluate participants’ models.

The ShopBench benchmark focused on four key shopping skills, along with a fifth “all-in-one” challenge:

1. Shopping Concept Understanding: Decoding complex shopping concepts and terminologies.
2. Shopping Knowledge Reasoning: Making informed decisions with shopping knowledge.
3. User Behavior Alignment: Understanding dynamic customer behavior.
4. Multilingual Abilities: Shopping across languages.
5. All-Around: Solving all tasks from the previous tracks in a unified solution.

NVIDIA’s Winning Solution

NVIDIA’s winning solution involved creating a single model for each track.

The team fine-tuned the just-released Qwen2-72B model using eight NVIDIA A100 Tensor Core GPUs for approximately 24 hours. The GPUs provided fast and efficient processing, significantly reducing the time required for fine-tuning.

First, the team generated training datasets based on the provided examples and synthesized additional data using Llama 3 70B hosted on build.nvidia.com.

Next, they employed QLoRA (Quantized Low-Rank Adaptation), a training process using the data created in step one. QLoRA modifies a smaller subset of the model’s weights, allowing efficient training and fine-tuning.

The model was then quantized — making it smaller and able to run on a system with a smaller hard drive and less memory — with AWQ 4-bit and used the vLLM inference library to predict the test datasets on four NVIDIA T4 Tensor Core GPUs within the time constraints.

This approach secured the top spot in each individual track and the overall first place in the competition—a clean sweep for NVIDIA for the second year in a row.

The team plans to submit a detailed paper on its solution next month and plans to present its findings at KDD 2024 in Barcelona.

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AI and accelerated computing — twin engines NVIDIA continuously improves — are delivering energy efficiency for many industries.

It’s progress the wider community is starting to acknowledge.

“Even if the predictions that data centers will soon account for 4% of global energy consumption become a reality, AI is having a major impact on reducing the remaining 96% of energy consumption,” said a report from Lisbon Council Research, a nonprofit formed in 2003 that studies economic and social issues.

The article from the Brussels-based research group is among a handful of big-picture AI policy studies starting to emerge. It uses Italy’s Leonardo supercomputer, accelerated with nearly 14,000 NVIDIA GPUs, as an example of a system advancing work in fields from automobile design and drug discovery to weather forecasting.

Why Accelerated Computing Is Sustainable Computing

Accelerated computing uses the parallel processing of NVIDIA GPUs to do more work in less time. As a result, it consumes less energy than general-purpose servers that employ CPUs built to handle one task at a time.

That’s why accelerated computing is sustainable computing.

The gains are even greater when accelerated systems apply AI, an inherently parallel form of computing that’s the most transformative technology of our time.

“When it comes to frontier applications like machine learning or deep learning, the performance of GPUs is an order of magnitude better than that of CPUs,” the report said.

User Experiences With Accelerated AI

Users worldwide are documenting energy-efficiency gains with AI and accelerated computing.

In financial services, Murex — a Paris-based company with a trading and risk-management platform used daily by more than 60,000 people — tested the NVIDIA Grace Hopper Superchip. On its workloads, the CPU-GPU combo delivered a 4x reduction in energy consumption and a 7x reduction in time to completion compared with CPU-only systems (see chart below).

“On risk calculations, Grace is not only the fastest processor, but also far more power-efficient, making green IT a reality in the trading world,” said Pierre Spatz, head of quantitative research at Murex.

In manufacturing, Taiwan-based Wistron built a digital copy of a room where NVIDIA DGX systems undergo thermal stress tests to improve operations at the site. It used NVIDIA Omniverse, a platform for industrial digitization, with a surrogate model, a version of AI that emulates simulations.

The digital twin, linked to thousands of networked sensors, enabled Wistron to increase the facility’s overall energy efficiency by up to 10%. That amounts to reducing electricity consumption by 120,000 kWh per year and carbon emissions by a whopping 60,000 kilograms.

Up to 80% Fewer Carbon Emissions

The RAPIDS Accelerator for Apache Spark can reduce the carbon footprint for data analytics, a widely used form of machine learning, by as much as 80% while delivering 5x average speedups and 4x reductions in computing costs, according to a recent benchmark.

Thousands of companies — about 80% of the Fortune 500 — use Apache Spark to analyze their growing mountains of data. Companies using NVIDIA’s Spark accelerator include Adobe, AT&T and the U.S. Internal Revenue Service.

In healthcare, Insilico Medicine discovered and put into phase 2 clinical trials a drug candidate for a relatively rare respiratory disease, thanks to its NVIDIA-powered AI platform.

Using traditional methods, the work would have cost more than $400 million and taken up to six years. But with generative AI, Insilico hit the milestone for one-tenth of the cost in one-third of the time.

“This is a significant milestone not only for us, but for everyone in the field of AI-accelerated drug discovery,” said Alex Zhavoronkov, CEO of Insilico Medicine.

This is just a sampler of results that users of accelerated computing and AI are pursuing at companies such as Amgen, BMW, Foxconn, PayPal and many more.

Speeding Science With Accelerated AI 

In basic research, the National Energy Research Scientific Computing Center (NERSC), the U.S. Department of Energy’s lead facility for open science, measured results on a server with four NVIDIA A100 Tensor Core GPUs compared with dual-socket x86 CPU servers across four of its key high-performance computing and AI applications.

Researchers found that the apps, when accelerated with the NVIDIA A100 GPUs, saw energy efficiency rise 5x on average (see below). One application, for weather forecasting, logged gains of nearly 10x.

Scientists and researchers worldwide depend on AI and accelerated computing to achieve high performance and efficiency.

In a recent ranking of the world’s most energy-efficient supercomputers, known as the Green500, NVIDIA-powered systems swept the top six spots, and 40 of the top 50.

Underestimated Energy Savings

The many gains across industries and science are sometimes overlooked in forecasts that extrapolate only the energy consumption of training the largest AI models. That misses the benefits from most of an AI model’s life when it’s consuming relatively little energy, delivering the kinds of efficiencies users described above.

In an analysis citing dozens of sources, a recent study debunked as misleading and inflated projections based on training models.

“Just as the early predictions about the energy footprints of e-commerce and video streaming ultimately proved to be exaggerated, so too will those estimates about AI likely be wrong,” said the report from the Information Technology and Innovation Foundation (ITIF), a Washington-based think tank.

The report notes as much as 90% of the cost — and all the efficiency gains — of running an AI model are in deploying it in applications after it’s trained.

“Given the enormous opportunities to use AI to benefit the economy and society — including transitioning to a low-carbon future — it is imperative that policymakers and the media do a better job of vetting the claims they entertain about AI’s environmental impact,” said the report’s author, who described his findings in a recent podcast.

Others Cite AI’s Energy Benefits

Policy analysts from the R Street Institute, also in Washington, D.C., agreed.

“Rather than a pause, policymakers need to help realize the potential for gains from AI,” the group wrote in a 1,200-word article.

“Accelerated computing and the rise of AI hold great promise for the future, with significant societal benefits in terms of economic growth and social welfare,” it said, citing demonstrated benefits of AI in drug discovery, banking, stock trading and insurance.

AI can make the electric grid, manufacturing and transportation sectors more efficient, it added.

AI Supports Sustainability Efforts

The reports also cited the potential of accelerated AI to fight climate change and promote sustainability.

“AI can enhance the accuracy of weather modeling to improve public safety as well as generate more accurate predictions of crop yields. The power of AI can also contribute to … developing more precise climate models,” R Street said.

The Lisbon report added that AI plays “a crucial role in the innovation needed to address climate change” for work such as discovering more efficient battery materials.

How AI Can Help the Environment

ITIF called on governments to adopt AI as a tool in efforts to decarbonize their operations.

Public and private organizations are already applying NVIDIA AI to protect coral reefs, improve tracking of wildfires and extreme weather, and enhance sustainable agriculture.

For its part, NVIDIA is working with hundreds of startups employing AI to address climate issues. NVIDIA also announced plans for Earth-2, expected to be the world’s most powerful AI supercomputer dedicated to climate science.

Enhancing Energy Efficiency Across the Stack

Since its founding in 1993, NVIDIA has worked on energy efficiency across all its products — GPUs, CPUs, DPUs, networks, systems and software, as well as platforms such as Omniverse.

In AI, the brunt of an AI model’s life is in inference, delivering insights that help users achieve new efficiencies. The NVIDIA GB200 Grace Blackwell Superchip has demonstrated 25x energy efficiency over the prior NVIDIA Hopper GPU generation in AI inference.

Over the last eight years, NVIDIA GPUs have advanced a whopping 45,000x in their energy efficiency running large language models (see chart below).

Recent innovations in software include TensorRT-LLM. It can help GPUs reduce 3x the energy consumption of LLM inference.

Here’s an eye-popping stat: If the efficiency of cars improved as much as NVIDIA has advanced the efficiency of AI on its accelerated computing platform, cars would get 280,000 miles per gallon. That means you could drive to the moon on less than a gallon of gas.

The analysis applies to the fuel efficiency of cars NVIDIA’s whopping 10,000x efficiency gain in AI training and inference from 2016 to 2025 (see chart below).

Driving Data Center Efficiency

NVIDIA delivers many optimizations through system-level innovations. For example, NVIDIA BlueField-3 DPUs can reduce power consumption up to 30% by offloading essential data center networking and infrastructure functions from less efficient CPUs.

Last year, NVIDIA received a $5 million grant from the U.S. Department of Energy — the largest of 15 grants from a pool of more than 100 applications — to design a new liquid-cooling technology for data centers. It will run 20% more efficiently than today’s air-cooled approaches and has a smaller carbon footprint.

These are just some of the ways NVIDIA contributes to the energy efficiency of data centers.

Data centers are among the most efficient users of energy and one of the largest consumers of renewable energy.

The ITIF report notes that between 2010 and 2018, global data centers experienced a 550% increase in compute instances and a 2,400% increase in storage capacity, but only a 6% increase in energy use, thanks to improvements across hardware and software.

NVIDIA continues to drive energy efficiency for accelerated AI, helping users in science, government and industry accelerate their journeys toward sustainable computing.

Try NVIDIA’s energy-efficiency calculator to find ways to improve energy efficiency. And check out NVIDIA’s sustainable computing site and corporate sustainability report for more information. 

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