Putting More Tech to the Test, NVIDIA Certifies New Categories of Gen AI-Ready Systems

Putting More Tech to the Test, NVIDIA Certifies New Categories of Gen AI-Ready Systems

Fueled by generative AI, enterprises globally are creating “AI factories,” where data comes in and intelligence comes out.

Critical to this movement are validated systems and reference architectures that reduce the risk and time involved in deploying specialized infrastructure that can support complex, computationally intensive generative AI workloads.

At the COMPUTEX trade show, NVIDIA today announced the expansion of its NVIDIA-Certified Systems program, which designates leading partner systems as suited for AI and accelerated computing, so customers can confidently deploy these platforms from the data center to the edge.

Two new certification types are now included: NVIDIA-Certified Spectrum-X Ready systems for AI in the data center and NVIDIA-Certified IGX systems for AI at the edge.

Each NVIDIA-Certified System undergoes rigorous testing and is validated to provide enterprise-grade performance, manageability, security and scalability for NVIDIA AI Enterprise software workloads, including generative AI applications built with NVIDIA NIM inference microservices. The systems provide a trusted pathway to design and implement efficient, reliable infrastructure.

NVIDIA-Certified Spectrum-X Ready Systems

The world’s first Ethernet fabric built for AI, the NVIDIA Spectrum-X AI Ethernet platform combines the NVIDIA Spectrum-4 SN5000 Ethernet switch series, NVIDIA BlueField-3 SuperNICs and networking acceleration software to deliver 1.6x AI networking performance over traditional Ethernet fabrics.

NVIDIA-Certified Spectrum-X Ready servers will act as building blocks for high-performance AI computing clusters and support powerful NVIDIA Hopper architecture and NVIDIA L40S GPUs.

NVIDIA-Certified IGX Systems

NVIDIA IGX Orin is an enterprise-ready AI platform for the industrial edge and medical applications that features industrial-grade hardware, a production-grade software stack and long-term enterprise support. It includes the latest technologies in device security, remote provisioning and management, along with built-in extensions, to deliver high-performance AI and proactive safety for low-latency, real-time applications in such areas as medical diagnostics, manufacturing, industrial robotics, agriculture and more.

Expanding Partner Portfolio

Top NVIDIA ecosystem partners are set to achieve the new certifications.

ASUS, Dell Technologies, GIGABYTE, Hewlett Packard Enterprise, Ingrasys, Lenovo, QCT and Supermicro will soon offer NVIDIA-Certified Spectrum-X Ready systems.

And NVIDIA-Certified IGX systems will soon be available from ADLINK, Advantech, Aetina, Ahead, Cosmo Intelligent Medical Devices (a division of Cosmo Pharmaceuticals), Dedicated Computing, Leadtek, Onyx and YUAN.

Learn more about NVIDIA-Certified Systems and the latest generative AI technologies by joining NVIDIA at COMPUTEX.

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Leading Medical Centers in Taiwan Adopt NVIDIA Accelerated Computing to Advance Biomedical Research

Leading Medical Centers in Taiwan Adopt NVIDIA Accelerated Computing to Advance Biomedical Research

Taiwan’s leading medical centers — the National Health Research Institute (NHRI) and Chang Gung Memorial Hospital (CGMH) — are set to advance biomedical research and healthcare for patients.

The centers are embracing accelerated computing and generative AI for everything from imaging to enhancing patient care, from streamlining clinical workflows to drug discovery research.

“The use of AI in healthcare will fundamentally change the way we approach disease prevention and treatment,” said Dr. Hung-Yi Chiou, director of the Institute of Population Health Sciences (IPHS) at NHRI. “With AI’s ability to analyze vast amounts of data quickly and accurately, we can develop personalized medicine strategies and early intervention methods that were previously unattainable.”

“The future of AI in healthcare is incredibly promising,” said Dr. Wen-Jin Cherng at CGMH.

With the assistance of AI in smart healthcare, future diagnoses will become more accurate, treatment plans will have better predictions and patients will experience faster recovery, Dr. Cherng explained. And in complex analytical processes, AI can enable more efficient and cost-effective decision-making in healthcare, he added.

“The transformative potential of the NVIDIA Blackwell platform allows us to integrate advanced AI capabilities into our medical practices, enhancing patient care and streamlining clinical workflows like never before,” he said.

NHRI, the leading medical research institution in Taiwan, plays a crucial role in advancing public health through biomedical research and innovation. The integration of NVIDIA accelerated computing into its IT infrastructure marks a significant leap forward in the realm of AI-driven healthcare.

NHRI’s collaboration with NVIDIA also extends to the development of large language models tailored specifically for Taiwan’s healthcare needs.

“Traditional Chinese medical records and genomic data present unique challenges that require localized solutions,” said Dr. Feng-Chi Chen, deputy director of IPHS at the NHRI.

These challenges include the complexity of language variations and the need for precise genomic interpretations specific to Taiwan’s population, Dr. Chen explained.

“NVIDIA accelerated computing enables us to create these solutions, ensuring that our healthcare system remains at the cutting edge of medical research,” he said.

CGMH, one of the largest healthcare systems in Taiwan, operates a network of 10 hospitals with a combined inpatient capacity of over 11,000 beds. It also serves millions of people in outpatient services. It’s a cornerstone of Taiwan’s healthcare system, which is one of the most advanced in the world.

“With the computational power of Blackwell, we can expand our language model services to all hospitals under our umbrella, enhancing professional support, patient care and streamlining clinical workflows,” said Dr. Chang-Fu Kuo, director of the AI center at CGMH. “It addresses the needs of various medical disciplines and diverse patient populations, enabling healthcare professionals to focus on critical clinical tasks and ultimately improve patient outcomes.”

NHRI, CGMH Pioneering Medical AI

NHRI currently uses six NVIDIA DGX A100 systems for cloud and data center services, focusing on biomedical model training and genomic analysis.

By harnessing the power of NVIDIA accelerated computing, NHRI is also tackling pressing public health issues. One of its key projects involves using AI to predict the risk of chronic diseases such as diabetes and cardiovascular conditions by analyzing a multitude of genetic and environmental parameters.

“This level of analysis was previously unattainable due to computational constraints,” said Dr. Chen. “Now, with the power of NVIDIA accelerated computing, we will be able to offer more accurate risk assessments and preventative strategies.”

CGMH also has a diverse array of NVIDIA hardware, including NVIDIA H100, A100, and other Tensor Core GPUs, which it uses for medical imaging development and deployment. The foundation serves 46 models daily and intends to use Blackwell for LLM training and the deployment of service robots in hospitals.

Running these systems on premises and keeping the data within the hospital’s infrastructure are key to ensuring patient data privacy as well as faster data processing and reduced latency, said Dr. Chihung Lin, deputy director of the CGMH AI center.

These technologies may be used in various medical applications, including:

  • Clinical Decision Support System: Developed on premises to ensure patient data confidentiality and privacy, this system assists clinicians by providing access to up-to-date data and guidelines and using models to answer questions and prepare medical decisions.
  • Patient Interaction System: Allows patients to interact with a robot to get answers about their medication and medical conditions, reducing the burden on medical staff. Medical staff review the robot’s responses to ensure accuracy.
  • Medical Imaging: Enhances radiology and other imaging tasks using AI. This project is one of the most mature AI technologies in CGMH’s healthcare system.
  • Precision Medicine: Handles large-scale genomic data and transforms sequences into readable medical reports for doctors. Focused on building computational facilities to support whole genome and exome sequencing.
  • Expansion of AI Services: Aims to extend the language model services to all hospitals under CGMH’s umbrella, leveraging the computational capacity from the Blackwell platform to support this expansion.

Other applications include early detection of colorectal cancer via endoscopy, autoimmune disease screening through microscope images and kidney disease prediction using general imaging techniques.

NHRI and CGMH’s adoption of accelerated computing underscores the growing importance of AI and advanced computing in medical research and healthcare delivery.

With these tools, Taiwan is poised to make strides in improving patient outcomes and advancing biomedical science.

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Power Tool: Generative AI Tracks Typhoons, Tames Energy Use

Power Tool: Generative AI Tracks Typhoons, Tames Energy Use

Weather forecasters in Taiwan had their hair blown back when they saw a typhoon up close, created on a computer that slashed the time and energy needed for the job.

It’s a reaction that users in many fields are feeling as generative AI shows them how new levels of performance contribute to reductions in total cost of ownership.

Inside the AI of the Storm

Tracking a typhoon provided a great test case of generative AI’s prowess. The work traditionally begins with clusters of CPUs cranking on complex algorithms to create atmospheric models with a 25-kilometer resolution.

Enter CorrDiff, a generative AI model that’s part of NVIDIA Earth-2, a set of services and software for weather and climate research.

Using a class of diffusion models that power today’s text-to-image services, CorrDiff resolved the 25-km models to two kilometers 1,000x faster, using 3,000x less energy for a single inference than traditional methods.

CorrDiff Cuts Costs 50x, Energy Use 25x

CorrDiff shines on the NVIDIA AI platform, even when retraining the model once a year and using statistical groups of a thousand forecasts to boost the accuracy of predictions. Compared to traditional methods under these conditions, it slashes cost by 50x cost and energy use by 25x a year.

That means work that used to require nearly $3 million for a cluster of CPUs and the energy to run them can be done for about $60,000 on a single system with an NVIDIA H100 Tensor Core GPU. It’s a massive reduction that shows how generative AI and accelerated computing increases energy efficiency and lowers total cost of ownership.

The technology also helps forecasters see more precisely where a typhoon will land, potentially saving lives.

“NVIDIA’s CorrDiff generative AI model opens the door to the use of AI-generated kilometer-scale weather forecasts, enabling Taiwan to prepare better for typhoons,” said Hongey Chen, a director of Taiwan’s National Science and Technology Center for Disaster Reduction.

The Taiwan forecasters could save nearly a gigawatt-hour a year, using CorrDiff. Energy savings could balloon if the nearly 200 regional weather data centers around the world adopt the technology for more sustainable computing.

Companies that sell commercial forecasts are also adopting CorrDiff, attracted by its speed and savings.

Broad Horizons for Energy Efficiency

NVIDIA Earth-2 takes these capabilities to a planetary scale. It fuses AI, physics simulations and observed data to help countries and companies respond to global issues like climate change. That will help address the impacts of climate change, which is expected to cost a million lives and $1.7 trillion per year by 2050.

Accelerated computing and generative AI are bringing new levels of performance and energy efficiency to many applications. Explainers on green computing and why GPUs are great for AI provide more context and some examples.

Compare the costs and energy consumption of popular workloads running on an x86 CPU-based server versus an NVIDIA GPU server with this simple calculator. And watch Huang’s keynote address at COMPUTEX to get the big picture.

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‘Create a Data Flywheel With AI,’ NVIDIA CEO Jensen Huang Tells Attendees at Snowflake Summit

‘Create a Data Flywheel With AI,’ NVIDIA CEO Jensen Huang Tells Attendees at Snowflake Summit

AI gives every company an opportunity to turn its processes into a data flywheel, NVIDIA founder and CEO Jensen Huang told thousands of attendees Monday at the Snowflake Data Cloud Summit.

Companies need to “take all the most important processes they do, capture them in a data flywheel and turn that into the company’s AI to drive that flywheel even further,” said Huang, joining from Taipei a virtual fireside chat with Snowflake’s CEO Sridhar Ramaswamy in San Francisco.

The two executives described how the combination of the Snowflake AI Data Cloud and NVIDIA AI will simplify and accelerate enterprise AI.

“You want to jump on this train as fast as you can, don’t let it fly by because you can use it to transform your business or go into new businesses,” said Huang, the day after he gave a keynote kicking off COMPUTEX in Taiwan.

Snowflake Users Can Tap Into NVIDIA AI Enterprise

For example, businesses will be able to deploy Snowflake Arctic, an enterprise-focused large language model (LLM), in seconds using NVIDIA NIM inference microservices, part of the NVIDIA AI Enterprise software platform. 

Arctic was trained on NVIDIA H100 Tensor Core GPUs and is available on the NVIDIA API catalog, fully supported by NVIDIA TensorRT-LLM, software that accelerates generative AI inference.

The two companies also will integrate Snowflake Cortex AI and NVIDIA NeMo Retriever, so businesses can link their AI-powered applications to information sources, ensuring highly accurate results with retrieval-augmented generation (RAG).

Ramaswamy gave examples of generative AI applications developed with the NVIDIA NeMo framework and Snowpark Container Services that will be available on Snowflake Marketplace for use by thousands of Snowflake’s customers.

“NVIDIA’s industry-leading accelerated computing is game changing for our customers and our own research team that used it to create the state-of-the-art Artic model for our customers,” said Ramaswamy.

To learn more, watch NVIDIA GTC on-demand sessions presented by Snowflake on how to build chatbots with a RAG architecture and how to leverage LLMs for life sciences.

 

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NVIDIA Grace Hopper Superchip Accelerates Murex MX.3 Analytics Performance, Reduces Power Consumption

NVIDIA Grace Hopper Superchip Accelerates Murex MX.3 Analytics Performance, Reduces Power Consumption

After the 2008 financial crisis and increased risk-management regulations that followed, Pierre Spatz anticipated banks would focus on reducing computing expenses.

As head of quantitative research at Murex, a trading and risk management software company based in Paris, Spatz adopted NVIDIA’s CUDA and GPU-accelerated computing, aiming for top performance and energy efficiency.

Always seeking the latest technologies, the company’s quants team has begun trials of the NVIDIA Grace Hopper Superchip. The effort is focused on helping customers better price and manage credit and market risk exposures of derivatives contracts.

More than 60,000 daily users in 65 countries rely on the Murex MX.3 platform. MX.3 assists banks, asset managers, pension funds and other financial institutions with their trading, risk and operations across asset classes.

Managing Risk With MX.3 Driven by Grace Hopper

Financial institutions need high-performance computing infrastructure to run risk models on vast amounts of data for pricing and risk calculations, and to deliver real-time decision-making capabilities.

MX.3 coverage includes both credit and market risk, fundamental review of the trading book and x-valuation adjustment (XVA). XVA is used for different types of valuation adjustments related to derivative contracts, such as the credit value adjustment (CVA), the margin value adjustment and the funding valuation adjustment.

Murex is testing Grace Hopper on the MX.3 platform for XVA calculations, as well as for market risk calibration, pricing evaluation, sensitivity, and profit and loss calculations on various asset classes.

Grace Hopper offers faster calculation as well as power savings to the Murex platform.

“On counterparty credit risk workloads such as CVA, Grace Hopper is the perfect fit, leveraging a heterogeneous architecture with a unique mix of CPU and GPU computations,” Spatz said. “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.”

When running XVA workloads in MX.3, the Murex research and development lab has noticed Grace Hopper can offer a 4x reduction in energy consumption and a 7x performance improvement compared with CPU-based systems.

Pricing FX Barrier Options in MX.3 With Grace Hopper 

To price foreign exchange (FX) barrier options, Murex has used its flagship and latest stochastic local volatility model and also noticed impressive performance improvements when running on Grace Hopper. A barrier option is a derivative with a payoff that relies on whether its underlying asset price reaches or crosses a specified threshold during the span of the option contract.

The pricing evaluation is done with a 2D partial differential equation, which is more cost-effective on the Arm-based NVIDIA Grace CPU in GH200. Pricing this derivative with MX.3 on Grace Hopper goes 2.3x faster compared with Intel Xeon Gold 6148.

The NVIDIA Grace CPU also offers significant power efficiencies for FX barrier calculations on a watts-per-server basis, and it’s 5x better.

NVIDIA’s next-generation accelerated computing platform is driving energy efficiency and cost-saving for high-performance computing for quantitative analytics in capital markets, says Murex, pointing to the results above.

Learn about NVIDIA AI solutions for financial services.

 

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Elevate Your Expertise: NVIDIA Introduces AI Infrastructure and Operations Training and Certification

Elevate Your Expertise: NVIDIA Introduces AI Infrastructure and Operations Training and Certification

NVIDIA has introduced a self-paced course, called AI Infrastructure and Operations Fundamentals, to provide enterprise professionals with essential training on the infrastructure and operational aspects of AI and accelerated computing. 

From enhancing speech recognition systems to powering self-driving cars, AI is transforming everyday life. The new course explains how to deploy and manage scalable infrastructure to support AI-based solutions, helping IT pros realize AI’s potential and stay competitive in the rapidly changing technological landscape. 

Course Overview  

The course is ideal for anyone seeking to expand their knowledge of AI and its applications. It was created and taught by NVIDIA experts with real-world experience and deep technical domain expertise.

The course is divided into three modules. The first, Introduction to AI, covers foundational AI concepts and principles. Learners will:   

  • Discover how AI is being applied in various sectors, to drive innovation and efficiency  
  • Trace the progression of AI from basic machine learning to advanced deep learning to generative AI — and learn how each phase unlocked new capabilities  
  • Explore how GPUs revolutionized AI, providing the computational power necessary for complex AI tasks  
  • Understand the importance of a robust software stack in ensuring optimal performance and efficiency  
  • Delve into the environments where AI workloads operate, whether on premises or in the cloud  

AI Infrastructure, the second module, dives into the critical infrastructure components that support AI operations. Learners will:  

  • Gain knowledge about the hardware that powers AI, including the latest advancements in compute platforms, networking and storage   
  • Explore practices that reduce data center carbon footprints and energy usage 
  • Discover how reference architectures can serve as a foundation for building the most effective AI designs  
  • Evaluate the benefits of transitioning from on-premises data centers to cloud-based solutions  

AI Operations, the final module, focuses on the practical aspect of managing AI infrastructure. Learners will:  

  • Gain insights into the tools and techniques that enable effective infrastructure management and monitoring  
  • Learn about orchestrating AI clusters and scheduling tasks to maximize performance and resource efficiency  

Certification: AI Infrastructure and Operations Associate  

Alongside the course, NVIDIA offers a new AI Infrastructure and Operations associate certification. This entry-level credential validates knowledge of the foundational concepts of adopting AI computing with NVIDIA solutions. Topics covered in this exam include: 

  • Accelerated computing use cases 
  • AI, machine learning and deep learning 
  • GPU architecture 
  • NVIDIA’s software suite 
  • Infrastructure and operation considerations for adopting NVIDIA solutions 

Whether attendees want to enhance existing skills, support projects, advance career paths, or embark on a new professional trajectory, this AI course and certification will further the knowledge and skills needed to excel in using AI. 

Learn more about this training and certification.  

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GeForce NOW Brings the Heat With ‘World of Warcraft’

GeForce NOW Brings the Heat With ‘World of Warcraft’

World of Warcraft comes to the cloud this week, part of the 17 games joining the GeForce NOW library, with seven available to stream this week.

Plus, it’s time to get rewarded. Get a free in-game mount in Elder Scrolls Online starting today by opting into GeForce NOW’s Rewards program.

Heroes Rise to the Cloud

Dive into the immersive realms of World of Warcraft, including the latest expansion Dragonflight, the nostalgic journey of World of Warcraft Classic and the recently launched World of Warcraft Cataclysm Classic. These popular, massively multiplayer, online role-playing experiences from Blizzard Entertainment immerse players in legendary battles.

World of Warcraft: Dragonflight on GeForce NOW
Dragonriders fly best in the cloud.

Embark on a journey of endless adventure in the rich and dynamic universe of Azeroth in the latest modern expansion, World of Warcraft: Dragonflight. The expansive landscapes of the Dragon Isles are available to explore — even on the back of a fearsome dragon. The newly awakened Dracthyr Evokers are also available, World of Warcraft’s first-ever playable race-and-class combo. GeForce NOW Priority and Ultimate members can get immersed in the cinematic gameplay with support for RTX ON.

World of Warcraft Cataclysm Classic on GeForce NOW
Witness the return of Deathwing.

Face the return of Deathwing the Destroyer, whose violent emergence shatters and reshapes the continent of Azeroth. Journey into an era of fire and destruction in World of Warcraft Cataclysm Classic and usher in a new era for Azeroth. The updated game brings new dungeons and raids, fresh race and class combinations, and more.

World of Warcraft Classic on GeForce NOW
Azeroth awaits.

Whether a seasoned adventurer or a newcomer to the game, head to the Azeroth of yesteryear in World of Warcraft Classic and relive the experience of the game as it was upon its initial launch, with a few new upgrades. Explore the Eastern Kingdoms and Kalimdor, venture into iconic dungeons or engage in legendary player-vs-player battles.

Experience it all with a GeForce NOW membership, which means no waiting for downloads or games to update, even for the upcoming World of Warcraft expansion The War Within.

Mount Up

GeForce NOW members get access to rewards that enhance the gaming experience. This week The Elder Scrolls Online 10-year celebration continues with an in-game reward for GeForce NOW members.

New member reward on GeForce NOW
Manes flow freely in the cloud.

Mounts offer a great way to travel the world and provide a completely different experience to traveling on foot. This new free reward provides members with a trusty companion beyond the starter option. The mount has a sunny disposition, matching its vibrant, multihued coat. It’s an excellent horse for a new rider or one who regularly ventures into treacherous situations.

Members can claim their free mount starting today by opting into rewards and checking their email for instructions on how to redeem. Ultimate and Priority members can redeem starting today, while free members will be able to claim it starting May 31. It’s available until June 30, first come first served.

New Games, Assemble!

Capes on GeForce NOW
Turn-based strategy with a superhero twist.

Build a team of heroes and fight to take back the city in Capes, a turn-based strategy game from Daedlic Entertainment. Recruit, train and deploy a team to take back the city from the villains that hold it hostage. Level up heroes to gain access to new abilities and powerful upgrades — plus, each hero gains a unique team-up ability from each of their allies.

Check out the full list of new games this week:

  • The Rogue Prince of Persia (New release on Steam, May 27)
  • Capes (New release on Steam, May 29)
  • Lords of the Fallen (New release on Xbox, available on PC Game Pass, May 30)
  • Soulmask (New release on Steam, May 31)
  • Path of Exile (Steam)
  • World of Warcraft: Dragonflight (Battle.net)
  • World of Warcraft Classic (Battle.net)
  • World of Warcraft Cataclysm Classic (Battle.net)

And members can look for the following later this month:

  • Autopsy Simulator (New release on Steam, June 6)
  • Chornobyl Liquidators (New release on Steam, June 6)
  • SunnySide (New release on Steam, June 14)
  • Still Wakes the Deep (New release on Steam and Xbox, available on PC Game Pass, June 18)
  • Disney Speedstorm (Steam and Xbox, available on PC Game Pass)
  • Farm Together 2 (Steam)
  • Resident Evil Village (Steam)
  • Star Traders: Frontiers (Steam)
  • Street Fighter 6 (Steam)
  • Torque Drift 2 (Epic Games Store)

More to May

In addition to the 24 games announced last month, four more joined the GeForce NOW library:

  • Senua’s Saga: Hellblade II (New release on Steam and Xbox, available on PC Game Pass, May 21)
  • Serum (New release on Steam, May 23)
  • Palworld (Steam, and Xbox, available on PC Game Pass)
  • Tomb Raider: Definitive Edition (Xbox, available on PC Game Pass)

Gestalt, Norland and Sunnyside have delayed their launch dates to later this year. Stay tuned to GFN Thursday for updates.

From Tamriel to Teyvet, Night City to Sanctuary, GeForce NOW brings the world of PC gaming to nearly any device. Share your favorite gaming destinations all month long using #GreetingsFromGFN for a chance to be featured on the @NVIDIAGFN channels.

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

https://x.com/NVIDIAGFN/status/1795847572793274591

 

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Riding the Wayve of AV 2.0, Driven by Generative AI

Riding the Wayve of AV 2.0, Driven by Generative AI

Generative AI is propelling AV 2.0, a new era in autonomous vehicle technology characterized by large, unified, end-to-end AI models capable of managing various aspects of the vehicle stack, including perception, planning and control.

London-based startup Wayve is pioneering this new era, developing autonomous driving technologies that can be built on NVIDIA DRIVE Orin and its successor NVIDIA DRIVE Thor, which uses the NVIDIA Blackwell GPU architecture designed for transformer, large language model (LLM) and generative AI workloads.

In contrast to AV 1.0’s focus on refining a vehicle’s perception capabilities using multiple deep neural networks, AV 2.0 calls for comprehensive in-vehicle intelligence to drive decision-making in dynamic, real-world environments.

Wayve, a member of the NVIDIA Inception program for cutting-edge startups, specializes in developing AI foundation models for autonomous driving, equipping vehicles with a “robot brain” that can learn from and interact with their surroundings.

“NVIDIA has been the oxygen of everything that allows us to train AI,” said Alex Kendall, cofounder and CEO of Wayve. “We train on NVIDIA GPUs, and the software ecosystem NVIDIA provides allows us to iterate quickly — this is what enables us to build billion-parameter models trained on petabytes of data.”

Generative AI also plays a key role in Wayve’s development process, enabling synthetic data generation so AV makers can use a model’s previous experiences to create and simulate novel driving scenarios.

The company is building embodied AI, a set of technologies that integrate advanced AI into vehicles and robots to transform how they respond to and learn from human behavior, enhancing safety.

Wayve recently announced its Series C investment round — with participation from NVIDIA — that will support the development and launch of the first embodied AI products for production vehicles. As Wayve’s core AI model advances, these products will enable manufacturers to efficiently upgrade cars to higher levels of driving automation, from L2+ assisted driving to L4 automated driving.

As part of its embodied AI development, Wayve launched GAIA-1, a generative AI model for autonomy that creates realistic driving videos using video, text and action inputs. It also launched LINGO-2, a driving model that links vision, language and action inputs to explain and determine driving behavior.

“One of the neat things about generative AI is that it allows you to combine different modes of data seamlessly,” Kendall said. “You can bring in the knowledge of all the texts, the general purpose reasoning and capabilities that we get from LLMs and apply that reasoning to driving — this is one of the more promising approaches that we know of to be able to get to true generalized autonomy and eventually L5 capabilities on the road.”

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Decoding How NVIDIA RTX AI PCs and Workstations Tap the Cloud to Supercharge Generative AI

Decoding How NVIDIA RTX AI PCs and Workstations Tap the Cloud to Supercharge Generative AI

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 GeForce RTX PC and RTX workstation users.

Generative AI is enabling new capabilities for Windows applications and games. It’s powering unscripted, dynamic NPCs, it’s enabling creators to generate novel works of art, and it’s helping gamers boost frame rates by up to 4x. But this is just the beginning.

As the capabilities and use cases for generative AI continue to grow, so does the demand for compute to support it.

Hybrid AI combines the onboard AI acceleration of NVIDIA RTX with scalable, cloud-based GPUs to effectively and efficiently meet the demands of AI workloads.

Hybrid AI, a Love Story

With growing AI adoption, app developers are looking for deployment options: AI running locally on RTX GPUs delivers high performance and low latency, and is always available — even when not connected to the internet. On the other hand, AI running in the cloud can run larger models and scale across many GPUs, serving multiple clients simultaneously. In many cases, a single application will use both.

Hybrid AI is a kind of matchmaker that harmonizes local PC and workstation compute with cloud scalability. It provides the flexibility to optimize AI workloads based on specific use cases, cost and performance. It helps developers ensure that AI tasks run where it makes the most sense for their specific applications.

Whether the AI is running locally or in the cloud it gets accelerated by NVIDIA GPUs and NVIDIA’s AI stack, including TensorRT and TensorRT-LLM. That means less time staring at pinwheels of death and more opportunity to deliver cutting-edge, AI powered features to users.

A range of NVIDIA tools and technologies support hybrid AI workflows for creators, gamers, and developers.

Dream in the Cloud, Bring to Life on RTX

Generative AI has demonstrated its ability to help artists ideate, prototype and brainstorm new creations. One such solution, the cloud-based Generative AI by iStock — powered by NVIDIA Edify — is a generative photography service that was built for and with artists, training only on licensed content and with compensation for artist contributors.

Generative AI by iStock goes beyond image generation, providing artists with extensive tools to explore styles, variations, modify parts of an image or expand the canvas. With all these tools, artists can ideate numerous times and still bring ideas to life quickly.

Once the creative concept is ready, artists can bring it back to their local systems. RTX-powered PCs and workstations offer artists AI acceleration in more than 125 of the top creative apps to realize the full vision — whether it’s creating an amazing piece of artwork in Photoshop with local AI tools, animating the image with a parallax effect in DaVinci Resolve, or building a 3D scene with the reference image in Blender with ray tracing acceleration, and AI denoising in Optix.

Hybrid ACE Brings NPCs to Life

Hybrid AI is also enabling a new realm of interactive PC gaming with NVIDIA ACE, allowing game developers and digital creators to integrate state-of-the-art generative AI models into digital avatars on RTX AI PCs.

Powered by AI neural networks, NVIDIA ACE lets developers and designers create non-playable characters (NPCs) that can understand and respond to human player text and speech. It leverages AI models, including speech-to-text models to handle natural language spoken aloud, to generate NPCs’ responses in real time.

A Hybrid Developer Tool That Runs Anywhere

Hybrid also helps developers build and tune new AI models. NVIDIA AI Workbench helps developers quickly create, test and customize pretrained generative AI models and LLMs on RTX GPUs. It offers streamlined access to popular repositories like Hugging Face, GitHub and NVIDIA NGC, along with a simplified user interface that enables data scientists and developers to easily reproduce, collaborate on and migrate projects.

Projects can be easily scaled up when additional performance is needed — whether to the data center, a public cloud or NVIDIA DGX Cloud — and then brought back to local RTX systems on a PC or workstation for inference and light customization. Data scientists and developers can leverage pre-built Workbench projects to chat with documents using retrieval-augmented generation (RAG), customize LLMs using fine-tuning, accelerate data science workloads with seamless CPU-to-GPU transitions and more.

The Hybrid RAG Workbench project provides a customizable RAG application that developers can run and adapt themselves. They can embed their documents locally and run inference either on a local RTX system, a cloud endpoint hosted on NVIDIA’s API catalog or using NVIDIA NIM microservices. The project can be adapted to use various models, endpoints and containers, and provides the ability for developers to quantize models to run on their GPU of choice.

NVIDIA GPUs power remarkable AI solutions locally on NVIDIA GeForce RTX PCs and RTX workstations and in the cloud. Creators, gamers and developers can get the best of both worlds with growing hybrid AI workflows.

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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Tidy Tech: How Two Stanford Students Are Building Robots for Handling Household Chores

Tidy Tech: How Two Stanford Students Are Building Robots for Handling Household Chores

Imagine having a robot that could help you clean up after a party — or fold heaps of laundry. Chengshu Eric Li and Josiah David Wong, two Stanford University Ph.D. students advised by renowned American computer scientist Professor Fei-Fei Li, are making that a ‌dream come true. In this episode of the AI Podcast, host Noah Kravitz spoke with the two about their project, BEHAVIOR-1K, which aims to enable robots to perform 1,000 household chores, including picking up fallen objects or cooking. To train the robots, they’re using the NVIDIA Omniverse platform, as well as reinforcement and imitation learning techniques. Listen to hear more about the breakthroughs and challenges Li and Wong experienced along the way.

Stay tuned for more AI Podcast episodes recorded live from GTC.

Time Stamps

3:33: Background on the BEHAVIOR-1K project

5:00: Why use a simulated environment to train robots? 

6:48: Why build a new simulation engine instead of using an existing one? 

10:48: The process of training the robots to perform household chores

14:04: Some of the most complex tasks taught to the robots

19:07: How are large language models and large vision models affecting the progress of robotics?

24:09: What’s next for the project?  

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