Posts in category: nVidia AI

Gamers love games — as do the people who make them.

GeForce NOW streams over 1,500 games from the cloud, and with the Game Developers Conference in full swing this week, today’s GFN Thursday celebrates all things games: the tech behind them, the tools that bring them to the cloud, the ways to play them and the new ones being added to the library this week.

Developers use a host of NVIDIA resources to deliver the best in PC cloud gaming experiences. CD PROJEKT RED, one of many developers to tap into these resources, recently announced a new update coming to Cyberpunk 2077 on April 11 — including a new technology preview for Ray Tracing: Overdrive Mode that enables full ray tracing on GeForce RTX 40 Series GPUs and RTX 4080 SuperPODs.

In addition, members in and around Sofia, Bulgaria, can now experience the best of GeForce NOW Ultimate cloud gaming. It’s the latest city to roll out RTX 4080 gaming rigs to GeForce NOW servers around the globe.

Plus, with five new games joining the cloud this week, and an upcoming marvel-ous reward, GeForce NOW members can look forward to a busy weekend of streaming goodness.

Developers, Mount Up!

GDC presents the ideal time to spotlight GeForce NOW tools that enable developers to seamlessly bring their games to the cloud. NVIDIA tools, software development kits (SDKs) and partner engines together enable the production of stunning real-time content that uses AI and ray tracing. And bringing these games to billions of non-PC devices is as simple as checking an opt-in box.

GeForce NOW taps into existing game stores, allowing game developers to reap the benefits of a rapidly growing audience without the hassle of developing for another platform. This means zero port work to bring games to the cloud. Users don’t have to buy games for another platform and can play them on many of the devices they already own.

Developers who want to do more have access to the GeForce NOW Developer Platform — an SDK and toolset empowering integration of, interaction with and testing on the NVIDIA cloud gaming service. It allows developers to enhance their games to run more seamlessly, add cloud gaming into their stores and launchers, and let users connect their accounts and libraries to GeForce NOW.

The SDK is a set of APIs, runtimes, samples and documentation that allows games to query for cloud execution and enable virtual touchscreens; launchers to trigger cloud streaming of a specified game; and GeForce NOW and publisher backends to facilitate account linking and game library ownership syncing, already available for Steam and Ubisoft games.

Content developers have a slew of opportunities to bring their virtual worlds and interactive experiences to users in unique ways, powered by the cloud.

Metaverse services company Improbable will use NVIDIA cloud gaming infrastructure for an interactive, live, invite-only experience that will accommodate up to 10,000 guests. Other recent developer events included the DAF Trucks virtual experience, where potential customers took the newest DAF truck for a test drive in a simulated world, with PixelMob’s Euro Truck Simulator 2 providing the virtual playground.

Furthermore, CD PROJEKT RED will be delivering full ray tracing, aka path tracing, to Cyberpunk 2077. Such effects were previously only possible for film and TV. With the power of a GeForce RTX 4080 gaming rig in the cloud, Ultimate members will be able to stream the new technology preview for the Ray Tracing: Overdrive Mode coming to Cyberpunk 2077 across devices — even Macs — no matter the game’s system requirements.

Get Ready for a Marvel-ous Week

GeForce NOW Ultimate members have been enjoying Marvel’s Midnight Suns’ ultra-smooth, cinematic game play thanks to DLSS 3 technology support on top of RTX-powered ray tracing, which together enable graphics breakthroughs.

Now, members can fight among the legends with Captain Marvel’s Medieval Marvel suit in a free reward, which will become available at the end of the month — first to Premium members who are opted into GeForce NOW rewards. This reward is only available until May 6, so upgrade to an Ultimate or Priority membership today and opt into rewards to get first access.

Next, on to the five new games hitting GeForce NOW week for a happy weekend:

• Tchia (New release on Epic Games Store)
• Chess Ultra (New release on Epic Games Store, March 23)
• Amberial Dreams (Steam)
• Symphony of War: The Nephilim Saga (Steam)
• No One Survived (Steam)

And with that, we’ve got a question to end this GFN Thursday:

You’ve got free rent for a year to live in a video game city of your choice, which one are you choosing?

— NVIDIA GeForce NOW (@NVIDIAGFN) March 22, 2023

Game developer CD PROJEKT RED today at the Game Developers Conference in San Francisco unveiled a technology preview for Cyberpunk 2077 with path tracing, coming April 11.

Path tracing, also known as full ray tracing, accurately simulates light throughout an entire scene. It’s used by visual effects artists to create film and TV graphics that are indistinguishable from reality. But until the arrival of GeForce RTX GPUs with RT Cores, and the AI-powered acceleration of NVIDIA DLSS, real-time video game path tracing was impossible because it is extremely GPU intensive.

“This not only gives better visuals to the players but also has the promise to revolutionize the entire pipeline of how games are being created,” said Pawel Kozlowski, a senior technology developer engineer at NVIDIA.

This technology preview, Cyberpunk 2077’s Ray Tracing: Overdrive Mode, is a sneak peak into the future of full ray tracing. With full ray tracing, now practically all light sources cast physically correct soft shadows. Natural colored lighting also bounces multiple times throughout Cyberpunk 2077’s world, creating more realistic indirect lighting and occlusion.

Cyberpunk 2077, previously an early adopter of ray tracing, becomes the latest modern blockbuster title to harness real-time path tracing. Coming shortly after path tracing for Minecraft, Portal and Quake II, it underscores a wave of adoption in motion.

Like with ray tracing, it’s expected many more will follow. And the influence on video games is just the start, as real-time path tracing holds promise for many design industries.

Decades of Research Uncorked

Decades in the making, real-time path tracing is indeed a big leap in gaming graphics.

While long used in computer-generated imagery for movies, path tracing there took place in offline rendering farms, often requiring hours to render a single frame.

In gaming, which requires fast frame rates, rendering needs to happen in about 0.016 seconds.

Since the 1970s, video games have relied on rasterization techniques (see below). More recently, in 2018, NVIDIA introduced RTX GPUs to support ray tracing. Path tracing is the final frontier for the most physically accurate lighting and shadows.

Path tracing has been one of the main lighting algorithms used in offline rendering farms and computer graphics in films for years. It wasn’t until GeForce RTX 40 series and DLSS 3 was available that it was possible to bring path tracing to real-time graphics.

Cyberpunk 2077 also taps into Shader Execution Reordering — available for use on the NVIDIA Ada Lovelace architecture generation — which optimizes GPU workloads, enabling more efficient path-traced lighting.

Accelerated by DLSS 3  

DLSS 3 complements groundbreaking advancements in path tracing and harnesses modern AI — built on GPU-accelerated deep learning, a form of neural networking — as a powerful gaming performance multiplier. DLSS allows games to render 1/8th of the pixels, then uses AI and GeForce RTX Tensor Cores to reconstruct the rest, dramatically multiplying frame rates, while delivering crisp, high-quality images that rival native resolution.

Running on Ada Lovelace advances — launched with GeForce RTX 40 Series GPUs — DLSS 3 multiplies frame rates, maintaining image quality and responsiveness in games.

Powerful Tools Now Available 

For game developers, NVIDIA today at GDC announced the availability of the RTX+ Path Tracing SDK 1.0. The package of technologies includes DLSS 3, Shader Execution Reordering (SER), RTX Direct Illumination (RTXDI) and NVIDIA Real-Time Denoisers (NRD).

Learn more about full RTX path tracing. 

And catch up on all the breakthroughs in generative AI and the metaverse by joining us at GTC this week.

Gamers wanted better graphics. GPUs delivered. Those GPUs became the key to the world-changing AI revolution. Now gamers are reaping the benefits.

At GDC 2023 in San Francisco this week, the gaming industry’s premier developers conference, NVIDIA made a series of announcements, including new games and game development tools that promise to accelerate innovations at the intersection of neural networking and graphics, or neural graphics revolution.

DLSS 3 harnesses modern AI — built on GPU-accelerated deep learning, a form of neural networking — as a powerful gaming performance multiplier. DLSS allows games to render 1/8th of the pixels, then uses AI and GeForce RTX Tensor Cores to reconstruct the rest, dramatically multiplying frame rates, while delivering crisp, high-quality images that rival native resolution.

It’s just one example of how gamers are benefiting from the advancements in AI supercomputing showcased at this week’s NVIDIA GTC technology conference, which is running concurrently with GDC. And game developers are adopting DLSS at a break-neck pace.

DLSS complements ground-breaking advancements in ray tracing — a technology long used by filmmakers — to bring richer, more immersive visual experiences to gamers in real time.

Thanks, in part, to DLSS, real-time ray tracing, considered by many to be impossible in 2017, exploded onto the gaming scene with the debut of NVIDIA RTX in 2018. Ray tracing is now everywhere in games.

At GDC, game developer CD PROJEKT RED announced Cyberpunk 2077 will activate full ray tracing, also called path tracing, with the upcoming technology preview of Ray Tracing: Overdrive Mode in Cyberpunk 2077.

Cyberpunk 2077, previously an early adopter of ray tracing, becomes the latest modern blockbuster title to harness real-time path tracing following  Minecraft, Portal and Quake II.

New DLSS 3 AAA Games Coming

During GDC, NVIDIA also announced the addition of DLSS 3 support for even more popular AAA games, including Diablo IV, Forza Horizon 5 and Redfall. 

• Diablo IV, the latest installment of the genre-defining Diablo franchise with multiple games, will launch on June 6 with DLSS 3, with ray tracing coming post-launch.
• Forza Horizon 5, named the best open-world racing game of all time, will update to DLSS 3 on March 28. 
• Redfall, Bethesda’s highly anticipated, open-world, co-op first-person shooter from Arkane Austin is launching on May 2 with DLSS 3, with ray tracing coming post-launch.

DLSS is now available in 270+ games and apps, and DLSS 3 is multiplying performance in 28 released games and has been adopted 7x faster than DLSS 2 in the first six months of their respective launches.

DLSS Frame Generation Now Publicly Available for Developers

NVIDIA announced DLSS Frame Generation is now publicly available for developers to integrate into their games and applications.

The public release of DLSS Frame Generation plug-ins will allow even more developers to adopt the framerate-boosting technology.

DLSS Frame Generation is now available via NVIDIA Streamline, an open-source, cross-vendor framework that simplifies the integration of super-resolution technologies in 3D games and apps.

For all the details, dig into our full coverage on GeForce News.

Unreal Engine 5.2 Integration to Speed Up DLSS 3 Adoption

At GDC, NVIDIA and Epic announced the integration of DLSS 3 into the popular Unreal Engine game engine.

Unreal Engine is an open and advanced real-time 3D creation tool that gives game developers and creators the freedom and control to deliver cutting-edge 3D content, interactive experiences and immersive virtual worlds.

A DLSS 3 plug-in will debut in UE 5.2, making it more straightforward for any developer to accelerate the performance of their games and applications, further accelerating the adoption of DLSS.

Cyberpunk 2077: A Showcase for What’s Next

CD PROJEKT RED showcases a technology preview of path tracing with Cyberpunk 2077.

Path tracing, also known as full ray tracing, allows developers to create cinematic experiences. Simulating the physics of light, using ray tracing as part of a neural graphics system, it’s capable of photorealism in 3D settings for more dynamic lighting and shadows.

GeForce gamers will be able to activate full ray tracing with the upcoming technology preview of Ray Tracing: Overdrive Mode on April 11.

These advancements are anchored in NVIDIA RTX technologies. To bring these incredible effects to life, CD PROJEKT RED and NVIDIA have worked hand in hand to integrate NVIDIA DLSS 3 and introduce new optimizations for this entirely new, fully ray-traced pipeline.

NVIDIA Shader Execution Reordering helps GPUs execute incoherent workloads boosting performance; NVIDIA Real-Time Denoisers have been used to improve performance and image quality.

As a result, with full ray tracing, now practically all light sources cast physically correct soft shadows. Natural colored lighting also bounces multiple times throughout Cyberpunk 2077’s world, creating more realistic indirect lighting and occlusion.

More to Come

Cyberpunk 2077 is a case study of how GPUs have unlocked the AI revolution and will bring great experiences to PC gamers for years to come.

An expanding game roster, game engine support and continued improvements in performance and image quality are securing NVIDIA DLSS as a landmark technology for the neural graphics revolution in PC gaming.

Like old friends catching up over coffee, two industry icons reflected on how modern AI got its start, where it’s at today and where it needs to go next.

Jensen Huang, founder and CEO of NVIDIA, interviewed AI pioneer Ilya Sutskever in a fireside chat at GTC. The talk was recorded a day after the launch of GPT-4, the most powerful AI model to date from OpenAI, the research company Sutskever co-founded.

They talked at length about GPT-4 and its forerunners, including ChatGPT. That generative AI model, though only a few months old, is already the most popular computer application in history.

Their conversation touched on the capabilities, limits and inner workings of the deep neural networks that are capturing the imaginations of hundreds of millions of users.

Compared to ChatGPT, GPT-4 marks a “pretty substantial improvement across many dimensions,” said Sutskever, noting the new model can read images as well as text.

“In some future version, [users] might get a diagram back” in response to a query, he said.

Under the Hood With GPT

“There’s a misunderstanding that ChatGPT is one large language model, but there’s a system around it,” said Huang.

In a sign of that complexity, Sutskever said OpenAI uses two levels of training.

The first stage focuses on accurately predicting the next word in a series. Here, “what the neural net learns is some representation of the process that produced the text, and that’s a projection of the world,” he said.

The second “is where we communicate to the neural network what we want, including guardrails … so it becomes more reliable and precise,” he added.

Present at the Creation

While he’s at the swirling center of modern AI today, Sutskever was also present at its creation.

In 2012, he was among the first to show the power of deep neural networks trained on massive datasets. In an academic contest, the AlexNet model he demonstrated with AI pioneers Geoff Hinton and Alex Krizhevsky recognized images faster than a human could.

Huang referred to their work as the Big Bang of AI.

The results “broke the record by such a large margin, it was clear there was a discontinuity here,” Huang said.

The Power of Parallel Processing

Part of that breakthrough came from the parallel processing the team applied to its model with GPUs.

“The ImageNet dataset and a convolutional neural network were a great fit for GPUs that made it unbelievably fast to train something unprecedented,” Sutskever said.

That early work ran on a few GeForce GTX 5080 GPUs in a University of Toronto lab. Today, tens of thousands of the latest NVIDIA A100 and H100 Tensor Core GPUs in the Microsoft Azure cloud service handle training and inference on models like ChatGPT.

“In the 10 years we’ve known each other, the models you’ve trained [have grown by] about a million times,” Huang said. “No one in computer science would have believed the computation done in that time would be a million times larger.”

“I had a very strong belief that bigger is better, and a goal at OpenAI was to scale,” said Sutskever.

A Billion Words

Along the way, the two shared a laugh.

“Humans hear a billion words in a lifetime,” Sutskever said.

“Does that include the words in my own head,” Huang shot back.

“Make it 2 billion,” Sutskever deadpanned.

The Future of AI

They ended their nearly hour-long talk discussing the outlook for AI.

Asked if GPT-4 has reasoning capabilities, Sutskever suggested the term is hard to define and the capability may still be on the horizon.

“We’ll keep seeing systems that astound us with what they can do,” he said. “The frontier is in reliability, getting to a point where we can trust what it can do, and that if it doesn’t know something, it says so,” he added.

“Your body of work is incredible … truly remarkable,” said Huang in closing the session. “This has been one of the best beyond Ph.D. descriptions of the state of the art of large language models,” he said.

To get all the news from GTC, watch the keynote below.

Building AI applications is hard. Putting them to use across a business can be even harder.

Less than one-third of enterprises that have begun adopting AI actually have it in production, according to a recent IDC survey.

Businesses often realize the full complexity of operationalizing AI just prior to launching an application. Problems discovered so late can seem insurmountable, so the deployment effort is often stalled and forgotten.

To help enterprises get AI deployments across the finish line, more than 100 machine learning operations (MLOps) software providers are working with NVIDIA. These MLOps pioneers provide a broad array of solutions to support businesses in optimizing their AI workflows for both existing operational pipelines and ones built from scratch.

Many NVIDIA MLOps and AI platform ecosystem partners as well as DGX-Ready Software partners, including Canonical, ClearML, Dataiku, Domino Data Lab, Run:ai and Weights & Biases, are building solutions that integrate with NVIDIA-accelerated infrastructure and software to meet the needs of enterprises operationalizing AI.

NVIDIA cloud service provider partners Amazon Web Services, Google Cloud, Azure, Oracle Cloud as well as other partners around the globe, such as Alibaba Cloud, also provide MLOps solutions to streamline AI deployments.

NVIDIA’s leading MLOps software partners are verified and certified for use with the NVIDIA AI Enterprise software suite, which provides an end-to-end platform for creating and accelerating production AI. Paired with NVIDIA AI Enterprise, the tools from NVIDIA’s MLOps partners help businesses develop and deploy AI successfully.

Enterprises can get AI up and running with help from these and other NVIDIA MLOps and AI platform partners:

• Canonical: Aims to accelerate at-scale AI deployments while making open source accessible for AI development. Canonical announced that Charmed Kubeflow is now certified as part of the DGX-Ready Software program, both on single-node and multi-node deployments of NVIDIA DGX systems. Designed to automate machine learning workflows, Charmed Kubeflow creates a reliable application layer where models can be moved to production.
• ClearML: Delivers a unified, open-source platform for continuous machine learning — from experiment management and orchestration to increased performance and ML production — trusted by teams at 1,300 enterprises worldwide. With ClearML, enterprises can orchestrate and schedule jobs on personalized compute fabric. Whether on premises or in the cloud, businesses can enjoy enhanced visibility over infrastructure usage while reducing compute, hardware and resource spend to optimize cost and performance. Now certified to run NVIDIA AI Enterprise, ClearML’s MLOps platform is more efficient across workflows, enabling greater optimization for GPU power.
• Dataiku: As the platform for Everyday AI, Dataiku enables data and domain experts to work together to build AI into their daily operations. Dataiku is now certified as part of the NVIDIA DGX-Ready Software program, which allows enterprises to confidently use Dataiku’s MLOps capabilities along with NVIDIA DGX AI supercomputers.
• Domino Data Lab: Offers a single pane of glass that enables the world’s most sophisticated companies to run data science and machine learning workloads in any compute cluster — in any cloud or on premises in all regions. Domino Cloud, a new fully managed MLOps platform-as-a-service, is now available for fast and easy data science at scale. Certified to run on NVIDIA AI Enterprise last year, Domino Data Lab’s platform mitigates deployment risks and ensures reliable, high-performance integration with NVIDIA AI.
• Run:ai: Functions as a foundational layer within enterprises’ MLOps and AI Infrastructure stacks through its AI computing platform, Atlas. The platform’s automated resource management capabilities allow organizations to properly align resources across different MLOps platforms and tools running on top of Run:ai Atlas. Certified to offer NVIDIA AI Enterprise, Run:ai is also fully integrating NVIDIA Triton Inference Server, maximizing the utilization and value of GPUs in AI-powered environments.
• Weights & Biases (W&B): Helps machine learning teams build better models, faster. With just a few lines of code, practitioners can instantly debug, compare and reproduce their models — all while collaborating with their teammates. W&B is trusted by more than 500,000 machine learning practitioners from leading companies and research organizations around the world. Now validated to offer NVIDIA AI Enterprise, W&B looks to accelerate deep learning workloads across computer vision, natural language processing and generative AI.

NVIDIA cloud service provider partners have integrated MLOps into their platforms that provide NVIDIA accelerated computing and software for data processing, wrangling, training and inference:

• Amazon Web Services: Amazon SageMaker for MLOps helps developers automate and standardize processes throughout the machine learning lifecycle, using NVIDIA accelerated computing. This increases productivity by training, testing, troubleshooting, deploying and governing ML models.
• Google Cloud: Vertex AI is a fully managed ML platform that helps fast-track ML deployments by bringing together a broad set of purpose-built capabilities. Vertex AI’s end-to-end MLOps capabilities make it easier to train, orchestrate, deploy and manage ML at scale, using NVIDIA GPUs optimized for a wide variety of AI workloads. Vertex AI also supports leading-edge solutions such as the NVIDIA Merlin framework, which maximizes performance and simplifies model deployment at scale. Google Cloud and NVIDIA collaborated to add Triton Inference Server as a backend on Vertex AI Prediction, Google Cloud’s fully managed model-serving platform.
• Azure: The Azure Machine Learning cloud platform is accelerated by NVIDIA and unifies ML model development and operations (DevOps). It applies DevOps principles and practices — like continuous integration, delivery and deployment — to the machine learning process, with the goal of speeding experimentation, development and deployment of Azure machine learning models into production. It provides quality assurance through built-in responsible AI tools to help ML professionals develop fair, explainable and responsible models.
• Oracle Cloud: Oracle Cloud Infrastructure (OCI) AI Services is a collection of services with prebuilt machine learning models that make it easier for developers to apply NVIDIA-accelerated AI to applications and business operations. Teams within an organization can reuse the models, datasets and data labels across services. OCI AI Services makes it possible for developers to easily add machine learning to apps without slowing down application development.
• Alibaba Cloud: Alibaba Cloud Machine Learning Platform for AI provides an all-in-one machine learning service featuring low user technical skills requirements, but with high performance results. Accelerated by NVIDIA, the Alibaba Cloud platform enables enterprises to quickly establish and deploy machine learning experiments to achieve business objectives.

Learn more about NVIDIA MLOps partners and their work at NVIDIA GTC, a global conference for the era of AI and the metaverse, running online through Thursday, March 23.

Watch NVIDIA founder and CEO Jensen Huang’s GTC keynote in replay:

ChatGPT is just the start.

With computing now advancing at what he called “lightspeed,” NVIDIA founder and CEO Jensen Huang today announced a broad set of partnerships with Google, Microsoft, Oracle and a range of leading businesses that bring new AI, simulation and collaboration capabilities to every industry.

“The warp drive engine is accelerated computing, and the energy source is AI,” Huang said in his keynote at the company’s GTC conference. “The impressive capabilities of generative AI have created a sense of urgency for companies to reimagine their products and business models.”

In a sweeping 78-minute presentation anchoring the four-day event, Huang outlined how NVIDIA and its partners are offering everything from training to deployment for cutting-edge AI services. He announced new semiconductors and software libraries to enable fresh breakthroughs. And Huang revealed a complete set of systems and services for startups and enterprises racing to put these innovations to work on a global scale.

Huang punctuated his talk with vivid examples of this ecosystem at work. He announced NVIDIA and Microsoft will connect hundreds of millions of Microsoft 365 and Azure users to a platform for building and operating hyperrealistic virtual worlds. He offered a peek at how Amazon is using sophisticated simulation capabilities to train new autonomous warehouse robots. He touched on the rise of a new generation of wildly popular generative AI services such as ChatGPT.

And underscoring the foundational nature of NVIDIA’s innovations, Huang detailed how, together with ASML, TSMC and Synopsis, NVIDIA computational lithography breakthroughs will help make a new generation of efficient, powerful 2-nm semiconductors possible.

The arrival of accelerated computing and AI come just in time, with Moore’s Law slowing and industries tackling powerful dynamics —sustainability, generative AI, and digitalization, Huang said. “Industrial companies are racing to digitalize and reinvent into software-driven tech companies — to be the disruptor and not the disrupted,” Huang said.

Acceleration lets companies meet these challenges. “Acceleration is the best way to reclaim power and achieve sustainability and Net Zero,” Huang said.

GTC: The Premier AI Conference

GTC, now in its 14th year, has become one of the world’s most important AI gatherings. This week’s conference features 650 talks from leaders such as Demis Hassabis of DeepMind, Valeri Taylor of Argonne Labs, Scott Belsky of Adobe, Paul Debevec of Netflix, Thomas Schulthess of ETH Zurich and a special fireside chat between Huang and Ilya Sutskever, co-founder of OpenAI, the creator of ChatGPT.

More than 250,000 registered attendees will dig into sessions on everything from restoring the lost Roman mosaics of 2,000 years ago to building the factories of the future, from exploring the universe with a new generation of massive telescopes to rearranging molecules to accelerate drug discovery, to more than 70 talks on generative AI.

The iPhone Moment of AI

NVIDIA’s technologies are fundamental to AI, with Huang recounting how NVIDIA was there at the very beginning of the generative AI revolution. Back in 2016 he hand-delivered to OpenAI the first NVIDIA DGX AI supercomputer — the engine behind the large language model breakthrough powering ChatGPT.

Launched late last year, ChatGPT went mainstream almost instantaneously, attracting over 100 million users, making it the fastest-growing application in history. “We are at the iPhone moment of AI,” Huang said.

NVIDIA DGX supercomputers, originally used as an AI research instrument, are now running 24/7 at businesses across the world to refine data and process AI, Huang reported. Half of all Fortune 100 companies have installed DGX AI supercomputers.

“DGX supercomputers are modern AI factories,” Huang said.

NVIDIA H100, Grace Hopper, Grace, for Data Centers

Deploying LLMs like ChatGPT are a significant new inference workload, Huang said.  For large-language-model inference, like ChatGPT, Huang announced a new GPU — the H100 NVL with dual-GPU NVLink.

Based on NVIDIA’s Hopper architecture, H100 features a Transformer Engine designed to process models such as the GPT model that powers ChatGPT. Compared to HGX A100 for GPT-3 processing, a standard server with four pairs of H100 with dual-GPU NVLink is up to 10x faster.

“H100 can reduce large language model processing costs by an order of magnitude,” Huang said.

Meanwhile, over the past decade, cloud computing has grown 20% annually into a $1 trillion industry, Huang said. NVIDIA designed the Grace CPU for an AI- and cloud-first world, where AI workloads are GPU accelerated. Grace is sampling now, Huang said.

NVIDIA’s new superchip, Grace Hopper, connects the Grace CPU and Hopper GPU over a high-speed 900GB/sec coherent chip-to-chip interface. Grace Hopper is ideal for processing giant datasets like AI databases for recommender systems and large language models, Huang explained.

“Customers want to build AI databases several orders of magnitude larger,” Huang said. “Grace Hopper is the ideal engine.”

DGX the Blueprint for AI Infrastructure

The latest version of DGX features eight NVIDIA H100 GPUs linked together to work as one giant GPU. “NVIDIA DGX H100 is the blueprint for customers building AI infrastructure worldwide,” Huang said, sharing that NVIDIA DGX H100 is now in full production.

H100 AI supercomputers are already coming online.

Oracle Cloud Infrastructure announced the limited availability of new OCI Compute bare-metal GPU instances featuring H100 GPUs

Additionally, Amazon Web Services announced its forthcoming EC2 UltraClusters of P5 instances, which can scale in size up to 20,000 interconnected H100 GPUs.

This follows Microsoft Azure’s private preview announcement last week for its H100 virtual machine, ND H100 v5.

Meta has now deployed its H100-powered Grand Teton AI supercomputer internally for its AI production and research teams.

And OpenAI will be using H100s on its Azure supercomputer to power its continuing AI research.

Other partners making H100 available include Cirrascale and CoreWeave, both which announced general availability today. Additionally, Google Cloud, Lambda, Paperspace and Vult are planning to offer H100.

And servers and systems featuring NVIDIA H100 GPUs are available from leading server makers including Atos, Cisco, Dell Technologies,  GIGABYTE, Hewlett Packard Enterprise, Lenovo and Supermicro.

DGX Cloud: Bringing AI to Every Company, Instantly

And to speed DGX capabilities to startups and enterprises racing to build new products and develop AI strategies, Huang announced NVIDIA DGX Cloud, through partnerships with Microsoft Azure, Google Cloud and Oracle Cloud Infrastructure to bring NVIDIA DGX AI supercomputers “to every company, from a browser.”

DGX Cloud is optimized to run NVIDIA AI Enterprise, the world’s leading acceleration software suite for end-to-end development and deployment of AI. “DGX Cloud offers customers the best of NVIDIA AI and the best of the world’s leading cloud service providers,” Huang said.

NVIDIA is partnering with leading cloud service providers to host DGX Cloud infrastructure, starting with Oracle Cloud Infrastructure. Microsoft Azure is expected to begin hosting DGX Cloud next quarter, and the service will soon expand to Google Cloud and more.

This partnership brings NVIDIA’s ecosystem to cloud service providers while amplifying NVIDIA’s scale and reach, Huang said. Enterprises will be able to rent DGX Cloud clusters on a monthly basis, ensuring they can quickly and easily scale the development of large, multi-node training workloads.

Supercharging Generative AI

To accelerate the work of those seeking to harness generative AI, Huang announced NVIDIA AI Foundations, a family of cloud services for customers needing to build, refine and operate custom LLMs and generative AI trained with their proprietary data and for domain-specific tasks.

AI Foundations services include NVIDIA NeMo for building custom language text-to-text generative models; Picasso, a visual language model-making service for customers who want to build custom models trained with licensed or proprietary content; and BioNeMo, to help researchers in the $2 trillion drug discovery industry.

Adobe is partnering with NVIDIA to build a set of next-generation AI capabilities for the future of creativity.

Getty Images is collaborating with NVIDIA to train responsible generative text-to-image and text-to-video foundation models.

Shutterstock is working with NVIDIA to train a generative text-to-3D foundation model to simplify the creation of detailed 3D assets.

Accelerating Medical Advances

And NVIDIA announced Amgen is accelerating drug discovery services with BioNeMo. In addition, Alchemab Therapeutics, AstraZeneca, Evozyne, Innophore and Insilico are all early access users of BioNemo.

BioNeMo helps researchers create, fine-tune and serve custom models with their proprietary data, Huang explained.

Huang also announced that NVIDIA and Medtronic, the world’s largest healthcare technology provider, are partnering to build an AI platform for software-defined medical devices. The partnership will create a common platform for Medtronic systems, ranging from surgical navigation to robotic-assisted surgery.

And today Medtronic announced that its GI Genius system, with AI for early detection of colon cancer, is built on NVIDIA Holoscan, a software library for real-time sensor processing systems, and will ship around the end of this year.

“The world’s $250 billion medical instruments market is being transformed,” Huang said.

Speeding Deployment of Generative AI Applications

To help companies deploy rapidly emerging generative AI models, Huang announced inference platforms for AI video, image generation, LLM deployment and recommender inference. They combine NVIDIA’s full stack of inference software with the latest NVIDIA Ada, Hopper and Grace Hopper processors — including the NVIDIA L4 Tensor Core GPU and the NVIDIA H100 NVL GPU, both launched today.

• NVIDIA L4 for AI Video can deliver 120x more AI-powered video performance than CPUs, combined with 99% better energy efficiency.

• NVIDIA L40 for Image Generation is optimized for graphics and AI-enabled 2D, video and 3D image generation.

• NVIDIA H100 NVL for Large Language Model Deployment is ideal for deploying massive LLMs like ChatGPT at scale.

• And NVIDIA Grace Hopper for Recommendation Models is ideal for graph recommendation models, vector databases and graph neural networks.

Google Cloud is the first cloud service provider to offer L4 to customers with the launch of its new G2 virtual machines, available in private preview today. Google is also integrating L4 into its Vertex AI model store.

Microsoft, NVIDIA to Bring Omniverse to ‘Hundreds of Millions’

Unveiling a second cloud service to speed unprecedented simulation and collaboration capabilities to enterprises, Huang announced NVIDIA is partnering with Microsoft to bring NVIDIA Omniverse Cloud, a fully managed cloud service, to the world’s industries.

“Microsoft and NVIDIA are bringing Omnivese to hundreds of millions of Microsoft 365 and Azure users,” Huang said, also unveiling new NVIDIA OVX servers and a new generation of workstations powered by NVIDIA RTX Ada Generation GPUs and Intel’s newest CPUs optimized for NVIDIA Omniverse.

To show the extraordinary capabilities of Omniverse, NVIDIA’s open platform built for 3D design collaboration and digital twin simulation, Huang shared a video showing how NVIDIA Isaac Sim, NVIDIA’s robotics simulation and synthetic generation platform, built on Omniverse, is helping Amazon save time and money with full-fidelity digital twins.

It shows how Amazon is working to choreograph the movements of Proteus, Amazon’s first fully autonomous warehouse robot, as it moves bins of products from one place to another in Amazon’s cavernous warehouses alongside humans and other robots.

Digitizing the $3 Trillion Auto Industry

Illustrating the scale of Omniverse’s reach and capabilities, Huang dug into Omniverse’s role in digitalizing the $3 trillion auto industry. By 2030, auto manufacturers will build 300 factories to make 200 million electric vehicles, Huang said, and battery makers are building 100 more megafactories. “Digitalization will enhance the industry’s efficiency, productivity and speed,” Huang said.

Touching on Omniverse’s adoption across the industry, Huang said Lotus is using Omniverse to virtually assemble welding stations. Mercedes-Benz uses Omniverse to build, optimize and plan assembly lines for new models. Rimac and Lucid Motors use Omniverse to build digital stores from actual design data that faithfully represent their cars.

Working with Idealworks, BMW uses Isaac Sim in Omniverse to generate synthetic data and scenarios to train factory robots. And BMW is using Omniverse to plan operations across factories worldwide and is building a new electric-vehicle factory, completely in Omniverse, two years before the plant opens, Huang said.

Separately. NVIDIA today announced that BYD, the world’s leading manufacturer of new energy vehicles NEVs, will extend its use of the NVIDIA DRIVE Orin centralized compute platform in a broader range of its NEVs.

Accelerating Semiconductor Breakthroughs

Enabling semiconductor leaders such as ASML, TSMC and Synopsis to accelerate the design and manufacture of a new generation of chips as current production processes near the limits of what physics makes possible, Huang announced NVIDIA cuLitho, a breakthrough that brings accelerated computing to the field of computational lithography.

The new NVIDIA cuLitho software library for computational lithography is being integrated by TSMC, the world’s leading foundry, as well as electronic design automation leader Synopsys into their software, manufacturing processes and systems for the latest-generation NVIDIA Hopper architecture GPUs.

Chip-making equipment provider ASML is working closely with NVIDIA on GPUs and cuLitho, and plans to integrate support for GPUs into all of their computational lithography software products. With lithography at the limits of physics, NVIDIA’s introduction of cuLitho enables the industry to go to 2nm and beyond, Huang said.

“The chip industry is the foundation of nearly every industry,” Huang said.

Accelerating the World’s Largest Companies

Companies around the world are on board with Huang’s vision.

Telecom giant AT&T uses NVIDIA AI to more efficiently process data and is testing Omniverse ACE and the Tokkio AI avatar workflow to build, customize and deploy virtual assistants for customer service and its employee help desk.

American Express, the U.S. Postal Service, Microsoft Office and Teams, and Amazon are among the 40,000 customers using the high-performance NVIDIA TensorRT inference optimizer and runtime, and NVIDIA Triton, a multi-framework data center inference serving software.

Uber uses Triton to serve hundreds of thousands of ETA predictions per second.

And with over 60 million daily users, Roblox uses Triton to serve models for game recommendations, build avatars, and moderate content and marketplace ads.

Microsoft, Tencent and Baidu are all adopting NVIDIA CV-CUDA for AI computer vision. The technology, in open beta, optimizes pre- and post-processing, delivering 4x savings in cost and energy.

Helping Do the Impossible

Wrapping up his talk, Huang thanked NVIDIA’s systems, cloud and software partners, as well as researchers, scientists and employees.

NVIDIA has updated 100 acceleration libraries, including cuQuantum and the newly open-sourced CUDA Quantum for quantum computing, cuOpt for combinatorial optimization, and cuLitho for computational lithography, Huang announced.

The global NVIDIA ecosystem, Huang reported, now spans 4 million developers, 40,000 companies and 14,000 startups in NVIDIA Inception.

“Together,” Huang said. “We are helping the world do the impossible.”

Companies across industries are looking to use interactive avatars to enhance digital experiences. But creating them is a complex, time-consuming process requiring state-of-the-art AI models that can see, hear, understand and communicate with end users.

To ease this process, NVIDIA is providing creators and developers with real-time AI solutions through Omniverse Avatar Cloud Engine (ACE), a suite of cloud-native microservices for end-to-end development of interactive avatars. In collaboration with early-access partners, NVIDIA is delivering improvements that will provide users with the tools they need to easily design and deploy various kinds of avatars, from interactive chatbots to intelligent digital humans.

AT&T and Quantiphi are among the first to experience how Omniverse ACE can help increase employee productivity and enhance customer service experiences.

Omniverse ACE users can now seamlessly integrate NVIDIA AI into their applications, including Riva for speech AI, NeMo service for natural language understanding, and Omniverse Audio2Face or Live Portrait for AI-powered 2D and 3D character animation.

With the latest improvements to Omniverse ACE, teams can also deploy advanced avatars across web conferencing and customer service use cases by integrating domain-specific NVIDIA AI workflows like Tokkio and Maxine.

Early Partners and Customers Develop AI-Driven Digital Humans

AT&T is planning to use Omniverse ACE and the Tokkio AI avatar workflow to build, customize and deploy virtual assistants for customer service and its employee help desk. Working with Quantiphi, one of NVIDIA’s service delivery partners, AT&T is developing interactive avatars that can provide 24/7 support in local languages across regions. This is helping the company reduce costs while providing a better experience for its employees worldwide.

In addition to customer service, AT&T is planning to build and develop digital humans for various use cases across the company.

“Quantiphi and NVIDIA have been collaborating to make customer experience more immersive by combining the power of large language models, graphics and recommender systems,” said Siddharth Kotwal, global head of NVIDIA Practice at Quantiphi. “NVIDIA’s Tokkio framework has made it easier to build, deploy and personalize AI-powered digital assistants or avatars for our enterprise customers. The process of seamlessly integrating automatic speech recognition, conversational agents and information retrieval systems with real-time animation has been simplified.”

Leading professional-services company Deloitte is also working with NVIDIA to help enterprises deploy transformative applications. Deloitte’s latest hybrid-cloud offerings — which consist of NVIDIA AI and Omniverse services and platforms, including Omniverse ACE — will be added to the Deloitte Center for AI Computing.

An Advanced, Streamlined Solution for Deploying Avatars

Omniverse ACE provides all the necessary tools so users can streamline the development process for realistic, intelligent avatars. Teams can also customize pre-built AI avatar workflows to suit their needs with applications like NVIDIA Tokkio. Additionally, Omniverse ACE is bringing new improvements to existing microservices.

Learn more about NVIDIA Omniverse ACE and register to join the early-access program, available now for developers.

Dive into the art of AI avatars at GTC, a global conference for the era of AI and the metaverse. Join sessions with NVIDIA and industry experts, and watch the GTC keynote below:

With AI at its tipping point, AI-enabled computer vision is being used to address the world’s most challenging problems in nearly every industry.

At GTC, a global conference for the era of AI and the metaverse running through Thursday, March 23, NVIDIA announced technology updates poised to drive the next wave of vision AI adoption. These include NVIDIA TAO Toolkit 5.0 for creating customized, production-ready AI models; expansions to the NVIDIA DeepStream software development kit for developing vision AI applications and services; and early access to Metropolis Microservices for powerful, cloud-native building blocks that accelerate vision AI.

Exploding Adoption and Ecosystem

More than 1,000 companies are using NVIDIA Metropolis developer tools to solve Internet of Things (IoT), sensor processing and operational challenges with vision AI — and the rate of adoption is quickening. The tools have now been downloaded over 1 million times by those looking to build vision AI applications.

PepsiCo is optimizing its operations with NVIDIA Metropolis to improve throughput, reduce downtime and minimize energy consumption.

The convenience-food and beverages giant is developing AI-powered digital twins of its distribution centers using the NVIDIA Omniverse platform to visualize how different setups in its facilities will impact operational efficiency before implementing them in the real world. PepsiCo is also using advanced machine vision technology, powered by the NVIDIA AI platform and GPUs, to improve efficiency and accuracy in its distribution process.

Siemens, a technology leader in industrial automation and digitalization, is adding next-level perception into its edge-based applications through NVIDIA Metropolis. With millions of sensors across factories, Siemens uses NVIDIA Metropolis — a key application framework for edge AI — to connect entire fleets of robots and IoT devices and bring AI into its industrial environments.

Automaker BMW Group is using computer vision technologies based on lidar and cameras — built by Seoul Robotics and powered by the NVIDIA Jetson edge AI platform — at its manufacturing facility in Munich to automate the movement of cars. This automation has resulted in significant time and cost savings, as well as employee safety improvements.

Making World-Class Vision AI Accessible to Any Developer on Any Device

As AI is made accessible to developers of any skill level, the next phase of AI adoption will arrive.

GTC is showcasing major expansions of Metropolis workflows, which put some of the latest AI capabilities and research into the hands of developers through NVIDIA TAO Toolkit, Metropolis Microservices and the DeepStream SDK, as well as the NVIDIA Isaac Sim synthetic data generation tool and robotics simulation applications.

NVIDIA TAO Toolkit is a low-code AI framework that supercharges vision AI model development for practically any developer, in any service, on any device. TAO 5.0 is filled with new features, including vision transformer pretrained AI models, the ability to deploy models on any platform with standard ONNX export, automatic hyperparameter tuning with AutoML, and AI-assisted data annotation.

STMicroelectronics, a global leader in embedded microcontrollers, integrates TAO into its STM32Cube AI developer workflow. TAO has enabled the company to run sophisticated AI in widespread IoT and edge use cases that STM32 microcontrollers power within their compute and memory budget.

The NVIDIA DeepStream SDK has emerged as a powerful tool for developers looking to create vision AI applications across a wide range of industries. With its latest update, a new graph execution runtime (GXF) allows developers to expand beyond the open-source GStreamer multimedia framework. DeepStream’s addition of GXF is a game-changer for users seeking to build applications that require tight execution control, advanced scheduling and critical thread management. This feature unlocks a host of new applications, including those in industrial quality control, robotics and autonomous machines.

Adding perception to physical spaces often requires applying vision AI to numerous cameras covering multiple regions.

Challenges in computer vision include monitoring the flow of packaged goods across a warehouse or analyzing individual customer flow across a large retail space. Metropolis Microservices make these sophisticated vision AI tasks easy to integrate and deploy into users’ applications.

Leading IT services company Infosys is using NVIDIA Metropolis to supercharge its vision AI application development and deployment. The NVIDIA TAO low-code training framework and pretrained models help Infosys reduce AI training efforts. Metropolis Microservices, along with the DeepStream SDK, optimize the company’s vision processing pipeline throughput and cut overall solution costs. Infosys can also generate troves of synthetic data with the NVIDIA Omniverse Replicator SDK to easily train AI models with new stock keeping units and packaging.

Latest Metropolis Features

Tap into the latest in NVIDIA vision AI technologies:

• Read the TAO 5.0 blog. Try TAO Toolkit on NVIDIA LaunchPad.
• GXF runtime, now part of NVIDIA DeepStream, unlocks new use cases that require tight scheduling control. Try it on NVIDIA LaunchPad.
• Sign up for early access to Metropolis Microservices, a suite of cloud-native microservices and reference applications that accelerate efforts to create API-driven solutions for the edge and the cloud.
• Learn more about NVIDIA Metropolis — through corporate blogs, technical blogs and case studies — to see how vision AI is transforming the world.

Register free to attend GTC, and watch these sessions to learn how to accelerate vision AI application development and understand its many use cases.

Watch NVIDIA founder and CEO Jensen Huang’s GTC keynote in replay:

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology improves creative workflows. We’re also deep diving on new GeForce RTX 40 Series GPU features, technologies and resources, and how they dramatically accelerate content creation.

Powerful AI technologies are revolutionizing 3D content creation — whether by enlivening realistic characters that show emotion or turning simple texts into imagery.

The brightest minds, artists and creators are gathering at NVIDIA GTC, a free, global conference on AI and the metaverse, taking place online through Thursday, March 23.

NVIDIA founder and CEO Jensen Huang’s GTC keynote announced a slew of advancements set to ease creators’ workflows, including using generative AI with the Omniverse Audio2Face app.

NVIDIA Omniverse, a platform for creating and operating metaverse applications, further expands with an updated Unreal Engine Connector, open-beta Unity Connector and new SimReady 3D assets.

New NVIDIA RTX GPUs, powered by the Ada Lovelace architecture, are fueling next-generation laptop and desktop workstations to meet the demands of the AI, design and the industrial metaverse.

The March NVIDIA Studio Driver adds support for the popular RTX Video Super Resolution feature, now available for GeForce RTX 40 and 30 Series GPUs.

And this week In the NVIDIA Studio, the Adobe Substance 3D art and development team explores the process of collaborating to create the animated short End of Summer using Omniverse USD Composer (formerly known as Create). 

Omniverse Overdrive

Specialized generative AI tools can boost creator productivity, even for users who don’t have extensive technical skills. Generative AI brings creative ideas to life, producing high-quality, highly iterative experiences — all in a fraction of the time and cost of traditional asset development.

The Omniverse Audio2Face AI-powered app allows 3D artists to efficiently animate secondary characters,  generating realistic facial animations with just an audio file — replacing what is often a tedious, manual process.

The latest release delivers significant upgrades in quality, usability and performance including a new headless mode and a REST API — enabling game developers and other creators to run the app and process numerous audio files from multiple users in the data center.

A new Omniverse Connector developed by NVIDIA for Unity workflows is available in open beta. Unity scenes can be added directly onto Omniverse Nucleus servers with access to platform features: the DeepSearch tool, thumbnails, bookmarks and more. Unidirectional live-sync workflows enable real-time updates.

With the Unreal Engine Connector’s latest release, Omniverse users can now use Unreal Engine’s USD import utilities to add skeletal mesh blend shape importing, and Python USD bindings to access stages on Omniverse Nucleus. This release also delivers improvements in import, export and live workflows, as well as updated software development kits.

In addition, over 1,000 new SimReady assets are available for creators. SimReady assets are built to real-world scale with accurate mass, physical materials and center of gravity for use within Omniverse PhysX for the most photorealistic visuals and accurate movements.

March Studio Driver Brings Superfly Super Resolution

Over 90% of online videos consumed by NVIDIA RTX GPU owners are 1080p resolution or lower, often resulting in upscaling that further degrades the picture despite the hardware being able to handle more.

The solution: RTX Video Super Resolution. The new feature, available on GeForce RTX 30 and 40 Series GPUs, uses AI to improve the quality of any video streamed through Google Chrome and Microsoft Edge browsers.

This improves video sharpness and clarity. Users can watch online content in its native resolution, even on high-resolution displays.

RTX Video Super Resolution is now available in the March Studio Driver, which can be downloaded today.

New NVIDIA RTX GPUs Power Professional Creators

Six new professional-grade NVIDIA RTX GPUs — based on the Ada Lovelace architecture — enable creators to meet the demands of their most complex workloads using laptops and desktops.

The NVIDIA RTX 5000, RTX 4000, RTX 3500, RTX 3000 and RTX 2000 Ada Generation laptop GPUs deliver up to 2x the performance compared with the previous generation. The NVIDIA RTX 4000 Small Form Factor (SFF) Ada Generation desktop GPU features new RT Cores, Tensor Cores and CUDA cores with up to 20GB of graphics memory.

These include the latest NVIDIA Max-Q and RTX technologies and are backed by the NVIDIA Studio platform with RTX optimizations in over 110 creative apps, NVIDIA RTX Enterprise Drivers for the highest levels of stability and performance, and exclusive AI-powered NVIDIA tools: Omniverse, Canvas and Broadcast.

Professionals using these laptop GPUs can run advanced technologies like DLSS 3 to increase frame rates by up to 4x compared to the previous generation, and Omniverse Enterprise for real-time collaboration and simulation.

Next-generation mobile workstations featuring NVIDIA RTX GPUs will be available starting this month.

Creative Boosts at GTC

• Experience GTC for more inspiring content, expert-led sessions and a must-see keynote to accelerate your life’s creative work.
• Catch these sessions on Omniverse, AI and 3D workflows — live or on demand:
• Fireside Chat With OpenAI Founder Ilya Sutskever and Jensen Huang: AI Today and Vision of the Future [S52092]
• How Generative AI Is Transforming the Creative Process: Fireside Chat With Adobe’s Scott Belsky and NVIDIA’s Bryan Catanzaro [S52090]
• Generative AI Demystified [S52089]
• 3D by AI: How Generative AI Will Make Building Virtual Worlds Easier [S52163]
• Custom World Building With AI Avatars: The Little Martians Sci-Fi Project [S51360]
• AI-Powered, Real-Time, Markerless: The New Era of Motion Capture [S51845]
• 3D and Beyond: How 3D Artists Can Build a Side Hustle in the Metaverse [SE52117]
• NVIDIA Omniverse User Group [SE52047]
• Accelerate the Virtual Production Pipeline to Produce an Award-Winning Sci-Fi Short Film [S51496]

As part of the Watch ‘n Learn Giveaway with valued partner 80LV, GTC attendees who register for any Omniverse for creators session — or watch on-demand before March 30 — have a chance to win a powerful GeForce RTX 4080 GPU. Simply fill out this form and tag #GTC23 and @NVIDIAOmniverse with the name of the session.

Search the GTC session catalog and check out the “Media and Entertainment” and “Omniverse” topics for additional creator-focused sessions.

A Father-Daughter Journey Back Home

The short animation End of Summer, created by the Substance 3D art and development team at Adobe, may evoke a surprising amount of heart. That was the team’s intent.

“We loved the idea of allowing the artwork to invoke an emotion in the viewer, letting them develop their own version of a story they felt was unfolding before their eyes,” said team member Wes McDermott.

End of Summer, a nod to stop-motion animation studios such as Laika, began as an internal Adobe Substance 3D project aimed at accomplishing two goals.

First, to encourage a relatively new group of artists to work together as a team by leaning into a creative endeavor. And second, to test their pipeline feature set for the potential of the Universal Scene Description (USD) framework.

The group divided the task of creating assets across the most popular 3D apps, including Adobe Substance 3D Modeler, Autodesk 3ds Max, Autodesk Maya, Blender and Maxon’s Cinema 4D. Their GeForce RTX GPUs unlocked AI denoising in the viewport for fast, interactive rendering and GPU-accelerated filters to speed up and simplify material creation.

“We quickly started to see the power of using USD as not only an export format but also a way to build assets,” McDermott said. “USD enables artists on the team to use whatever 3D app they felt most comfortable with.”

The Adobe team relied heavily on the Substance 3D asset library of materials, models and lights to create their studio environment. All textures were applied in Substance 3D Painter, where RTX-accelerated light and ambient occlusion baking optimized assets in mere moments.

Then, they imported all models into Omniverse USD Composer, where the team simultaneously refined and assembled assets.

“This was also during the pandemic, and we were all quarantined in our homes,” McDermott said. “Having a project we could work on together as a team helped us to communicate and be creative.”

Lastly, the artists imported the scene into Unreal Engine as a stage for lighting and rendering.

McDermott stressed the importance of hardware in his team’s workflows. “The bakers in Substance Painter are GPU accelerated and benefit greatly from NVIDIA RTX GPUs,” he said. “We were also heavily working on Unreal Engine and reliant on real-time rendering.”

For more on this workflow, check out the GTC session, 3D Art Goes Multiplayer: Behind the Scenes of Adobe Substance’s ‘End of Summer’ Project With Omniverse. Registration is free.

Check out McDermott’s portfolio on Instagram.

Follow NVIDIA Studio on Instagram, Twitter and Facebook. Access tutorials on the Studio YouTube channel and get updates directly in your inbox by subscribing to the Studio newsletter. Learn more about Omniverse on Instagram, Medium, Twitter and YouTube for additional resources and inspiration. Check out the Omniverse forums, and join our Discord server and Twitch channel to chat with the community.

The automotive industry is undergoing a digital revolution, driven by breakthroughs in accelerated computing, AI and the industrial metaverse.

Automakers are digitalizing every phase of the product lifecycle — including concept and styling, design and engineering, software and electronics, smart factories, autonomous driving and retail — using the NVIDIA Omniverse platform and AI.

Based on the Universal Scene Description (USD) framework, Omniverse transforms complex 3D workflows, allowing teams to connect and customize 3D pipelines and simulate large-scale, physically accurate virtual worlds. By taking the automotive product workflow into the virtual world, automakers can bypass traditional bottlenecks to save critical time and reduce cost.

Bringing Ideas to Life

Designing new vehicle models — and refreshing current ones — is a collaborative process that requires review and alignment of even the tiniest details.

By refining concepts in Omniverse, designers can visualize every facet of a car’s interior and exterior in the full context of the broader vehicle. Global teams can iterate quickly with real-time, physically based, photorealistic rendering. For example, they can collaborate to design the cockpit’s critical components, such as digital instrument clusters and infotainment systems, which must strike a balance of communicating information while minimizing distraction.

Omniverse enables designers to flexibly lay out the cabin and cockpit onscreen user experience along with the vehicle’s physical interior to ensure a harmonious look and feel.

With this next-generation design process, automakers can catch flaws early and make real-time improvements, reducing the number of physical prototypes to test and validate.

Virtual Validation

Once the design is complete, developers can use Omniverse to kick the tires on their new concepts.

Perfecting the interior is necessary for customer experience as well as safety.

Developers can take these in-cabin designs for a spin in the virtual world, collaborating and sharing designs for efficient refinement and validation.

Digitalization is also transforming the way automakers approach vehicle engineering. Teams can test different materials and components in a virtual environment to further reduce physical prototyping. For example, engineers can use computational fluid dynamics to refine aerodynamics and perform virtual crash simulations for safer vehicle designs.

Continuous Improvement

The coming generation of vehicles are highly advanced computers on wheels, packed with complex, centralized electronic systems and software for enhanced safety, intelligence and security.

Typically, vehicle functions are controlled by dozens of electronic control units distributed throughout a vehicle. By centralizing computing into core domains, automakers can replace many components and simplify what has been an incredibly complex supply chain.

With a digital representation of this entire architecture, automakers can simulate and test the vehicle software, and then provide over-the-air updates for continuous improvement throughout the car’s lifespan — from remote diagnostics to autonomous-driving capabilities to subscriptions for entertainment and other services.

Digital-First Production

Vehicle production is a colossal undertaking that requires thousands of parts and workers moving in sync. Any supply chain or production issues can lead to costly delays.

With Omniverse, automakers can develop and operate complex, AI-enabled virtual environments for factory and warehouse design. These physically based, precision-timed digital twins are the key to unlocking operational efficiencies with predictive analysis and process automation.

Factory planners can access the digital twin of the factory to review and improve the plant as needed. Every change can be quickly evaluated and validated in the virtual world, then implemented in the real world to ensure maximum efficiency and optimal ergonomics for factory workers.

Additionally, automakers can synchronize plant locations anywhere in the world for scalable design and iteration.

Autonomous Vehicle Proving Grounds

On top of enhancing traditional product development and manufacturing, Omniverse offers a complete toolchain for developing and validating automated and autonomous-driving systems.

NVIDIA DRIVE Sim is a physically based simulation platform, built on NVIDIA Omniverse, designed for fast and efficient autonomous-vehicle testing and validation at scale. It is time-accurate and supports the complete development toolchain, so developers can run simulation at the component level or for the entire system.

With DRIVE Sim, developers can repeatedly simulate routine driving scenarios, as well as rare and hazardous conditions that are too risky to test in the real world. Additionally, real-world driving recordings can be turned into reactive simulation scenarios using the platform’s Neural Reconstruction Engine.

Automakers can also fine-tune their advanced driver-assistance and autonomous-vehicle systems for New Car Assessment Program (NCAP) regulations, which evaluate the safety performance of new cars based on several crash tests and safety features.

The DRIVE Sim NCAP tool provides high-fidelity NCAP test protocols in simulation, so automakers can efficiently perform dedicated development and validation at scale.

The ability to drive in physically based virtual environments can significantly accelerate the autonomous-vehicle development process, overcoming data collection and scenario diversity hurdles that occur in real-world testing.

Omniverse’s generative AI reconstructs previously driven routes into 3D so past experiences can be reenacted or modified.

Try Before You Buy

The end customer benefits from digitalization, too.

Immersive technologies in Omniverse — including 3D visualization, augmented reality (AR) and virtual reality (VR) streamed using NVIDIA CloudXR — deliver consumers a more engaging experience, allowing them to explore features before making a purchase.

Prospective buyers can customize their vehicle in a car configurator — choosing colors, interior materials, trim levels and more — without being limited by the physical inventory of a dealership. They can then check out the car from every angle using 3D visualization. And with AR and VR, they can view and virtually test drive a car from anywhere.

The benefits of digitalization extend beyond the automotive industry. With Omniverse, any enterprise can reimagine their workflows to increase efficiency, productivity and speed, revolutionizing the way they do business. Omniverse is the digital-to-physical operating system to realize industrial digitalization.

Learn more about the latest in AI and the metaverse by watching NVIDIA founder and CEO Jensen Huang’s GTC keynote address: