Companies and organizations are increasingly using AI to protect their customers and thwart the efforts of fraudsters around the world. Voice security company Hiya found that 550 million scam calls were placed per week in 2023, with INTERPOL estimating that scammers stole $1 trillion from victims that same year. In the U.S., one of four noncontact-list calls were flagged as suspected spam, with fraudsters often luring people into Venmo-related or extended warranty scams.

Traditional methods of fraud detection include rules-based systems, statistical modeling and manual reviews. These methods have struggled to scale to the growing volume of fraud in the digital era without sacrificing speed and accuracy. For instance, rules-based systems often have high false-positive rates, statistical modeling can be time-consuming and resource-intensive, and manual reviews can't scale rapidly enough.

At yesterday's J.P. Morgan Healthcare Conference NVIDIA announced new partnerships to transform the $10 trillion healthcare and life sciences industry by accelerating drug discovery, enhancing genomic research and pioneering advanced healthcare services with agentic and generative AI. The convergence of AI, accelerated computing and biological data is turning healthcare into the largest technology industry. Healthcare leaders IQVIA, Illumina and Mayo Clinic, as well as Arc Institute, are using the latest NVIDIA technologies to develop solutions that will help advance human health.

These solutions include AI agents that can speed clinical trials by reducing administrative burden, AI models that learn from biology instruments to advance drug discovery and digital pathology, and physical AI robots for surgery, patient monitoring and operations. AI agents, AI instruments and AI robots will help address the $3 trillion of operations dedicated to supporting industry growth and create an AI factory opportunity in the hundreds of billions of dollars.

IQVIA, the world's leading provider of clinical research services, commercial insights and healthcare intelligence, is working with NVIDIA to build custom foundation models and agentic AI workflows that can accelerate research, clinical development and access to new treatments. AI applications trained on the organization's vast healthcare-specific information and guided by its deep domain expertise will help the industry boost the efficiency of clinical trials and optimize planning for the launch of therapies and medical devices—ultimately improving patient outcomes.

Operating in over 100 countries, IQVIA has built the largest global healthcare network and is uniquely connected to the ecosystem with the most comprehensive and granular set of information, analytics and technologies in the industry. Announced today at the J.P. Morgan Conference in San Francisco, IQVIA's collection of models, AI agents and reference workflows will be developed with the NVIDIA AI Foundry platform for building custom models, allowing IQVIA's thousands of pharmaceutical, biotech and medical device customers to benefit from NVIDIA's agentic AI capabilities and IQVIA's technologies, life sciences information and expertise.

NVIDIA announced today that Toyota, Aurora and Continental have joined the list of global mobility leaders developing and building their consumer and commercial vehicle fleets on NVIDIA accelerated computing and AI. Toyota, the world's largest automaker, will build its next-generation vehicles on NVIDIA DRIVE AGX Orin, running the safety-certified NVIDIA DriveOS operating system. These vehicles will offer functionally safe, advanced driving assistance capabilities.

The majority of today's auto manufacturers, truckmakers, robotaxi, and autonomous delivery vehicle companies, tier-one suppliers and mobility startups are developing on NVIDIA DRIVE AGX platform and technologies. With cutting-edge platforms spanning training in the cloud to simulation to compute in the car, NVIDIA's automotive vertical business is expected to grow to approximately $5 billion in fiscal year 2026.

NVIDIA today announced that its autonomous vehicle (AV) platform, NVIDIA DRIVE AGX Hyperion, has passed industry-safety assessments by TÜV SÜD and TÜV Rheinland—two of the industry's foremost authorities for automotive-grade safety and cybersecurity. This achievement raises the bar for AV safety, innovation and performance.

DRIVE Hyperion is the industry's first and only end-to-end autonomous driving platform. It includes the DRIVE AGX system-on-a-chip (SoC) and reference board design, the NVIDIA DriveOS automotive operating system, a sensor suite, and an active safety and level 2+ driving stack. Automotive safety pioneers such as Mercedes-Benz, JLR and Volvo Cars are adopting the platform, which is designed to be modular, so customers can easily use what they need. It is also scalable and built to be upgradeable and compatible across future DRIVE SoC generations.

NVIDIA today unveiled NVIDIA Project DIGITS, a personal AI supercomputer that provides AI researchers, data scientists and students worldwide with access to the power of the NVIDIA Grace Blackwell platform. Project DIGITS features the new NVIDIA GB10 Grace Blackwell Superchip, offering a petaflop of AI computing performance for prototyping, fine-tuning and running large AI models.

With Project DIGITS, users can develop and run inference on models using their own desktop system, then seamlessly deploy the models on accelerated cloud or data center infrastructure. "AI will be mainstream in every application for every industry. With Project DIGITS, the Grace Blackwell Superchip comes to millions of developers," said Jensen Huang, founder and CEO of NVIDIA. "Placing an AI supercomputer on the desks of every data scientist, AI researcher and student empowers them to engage and shape the age of AI."

NVIDIA today announced NVIDIA Cosmos, a platform comprising state-of-the-art generative world foundation models, advanced tokenizers, guardrails and an accelerated video processing pipeline built to advance the development of physical AI systems such as autonomous vehicles (AVs) and robots.

Physical AI models are costly to develop, and require vast amounts of real-world data and testing. Cosmos world foundation models, or WFMs, offer developers an easy way to generate massive amounts of photoreal, physics-based synthetic data to train and evaluate their existing models. Developers can also build custom models by fine-tuning Cosmos WFMs. Cosmos models will be available under an open model license to accelerate the work of the robotics and AV community. Developers can preview the first models on the NVIDIA API catalog, or download the family of models and fine-tuning framework from the NVIDIA NGC catalog or Hugging Face.

The 63rd edition of the TOP500 reveals that Frontier has once again claimed the top spot, despite no longer being the only exascale machine on the list. Additionally, a new system has found its way into the Top 10.

The Frontier system at Oak Ridge National Laboratory in Tennessee, USA remains the most powerful system on the list with an HPL score of 1.206 EFlop/s. The system has a total of 8,699,904 combined CPU and GPU cores, an HPE Cray EX architecture that combines 3rd Gen AMD EPYC CPUs optimized for HPC and AI with AMD Instinct MI250X accelerators, and it relies on Cray's Slingshot 11 network for data transfer. On top of that, this machine has an impressive power efficiency rating of 52.93 GFlops/Watt - putting Frontier at the No. 13 spot on the GREEN500.

A manufacturing plant near Hsinchu, Taiwan's Silicon Valley, is among facilities worldwide boosting energy efficiency with AI-enabled digital twins. A virtual model can help streamline operations, maximizing throughput for its physical counterpart, say engineers at Wistron, a global designer and manufacturer of computers and electronics systems. In the first of several use cases, the company built a digital copy of a room where NVIDIA DGX systems undergo thermal stress tests (pictured above). Early results were impressive.

Making Smart Simulations
Using NVIDIA Modulus, a framework for building AI models that understand the laws of physics, Wistron created digital twins that let them accurately predict the airflow and temperature in test facilities that must remain between 27 and 32 degrees C. A simulation that would've taken nearly 15 hours with traditional methods on a CPU took just 3.3 seconds on an NVIDIA GPU running inference with an AI model developed using Modulus, a whopping 15,000x speedup. The results were fed into tools and applications built by Wistron developers with NVIDIA Omniverse, a platform for creating 3D workflows and applications based on OpenUSD.

Dell Technologies is strengthening its collaboration with NVIDIA to help enterprises adopt AI technologies. By expanding the Dell Generative AI Solutions portfolio, including with the new Dell AI Factory with NVIDIA, organizations can accelerate integration of their data, AI tools and on-premises infrastructure to maximize their generative AI (GenAI) investments. "Our enterprise customers are looking for an easy way to implement AI solutions—that is exactly what Dell Technologies and NVIDIA are delivering," said Michael Dell, founder and CEO, Dell Technologies. "Through our combined efforts, organizations can seamlessly integrate data with their own use cases and streamline the development of customized GenAI models."

"AI factories are central to creating intelligence on an industrial scale," said Jensen Huang, founder and CEO, NVIDIA. "Together, NVIDIA and Dell are helping enterprises create AI factories to turn their proprietary data into powerful insights."

NVIDIA today announced its next-generation AI supercomputer—the NVIDIA DGX SuperPOD powered by NVIDIA GB200 Grace Blackwell Superchips—for processing trillion-parameter models with constant uptime for superscale generative AI training and inference workloads.

Featuring a new, highly efficient, liquid-cooled rack-scale architecture, the new DGX SuperPOD is built with NVIDIA DGX GB200 systems and provides 11.5 exaflops of AI supercomputing at FP4 precision and 240 terabytes of fast memory—scaling to more with additional racks.

Powering a new era of computing, NVIDIA today announced that the NVIDIA Blackwell platform has arrived—enabling organizations everywhere to build and run real-time generative AI on trillion-parameter large language models at up to 25x less cost and energy consumption than its predecessor.

The Blackwell GPU architecture features six transformative technologies for accelerated computing, which will help unlock breakthroughs in data processing, engineering simulation, electronic design automation, computer-aided drug design, quantum computing and generative AI—all emerging industry opportunities for NVIDIA.

During the 2024 SIEPR Economic Summit, NVIDIA CEO Jensen Huang acknowledged that the company's next-generation DGX systems, designed for AI and high-performance computing workloads, will require liquid cooling due to their immense power consumption. Huang also hinted that these new systems are set to be released in the near future. The revelation comes as no surprise, given the increasing power of GPUs needed to satisfy AI and machine learning applications. As computational requirements continue to grow, so does the need for more powerful hardware. However, with great power comes great heat generation, necessitating advanced cooling solutions to maintain optimal performance and system stability. Liquid cooling has long been a staple in high-end computing systems, offering superior thermal management compared to traditional air cooling methods.

By implementing liquid cooling in the upcoming DGX systems, NVIDIA aims to push the boundaries of performance while ensuring the hardware remains reliable and efficient. Although Huang did not provide a specific release date for the new DGX systems, his statement suggests that they are on the horizon. Whether the next generation of DGX systems uses the current NVIDIA H200 or the upcoming Blackwell B100 GPU as their primary accelerator, the performance will undoubtedly be delivered. As the AI and high-performance computing landscape continues to evolve, NVIDIA's position continues to strengthen, and liquid-cooled systems will certainly play a crucial role in shaping the future of these industries.

Nations have long invested in domestic infrastructure to advance their economies, control their own data and take advantage of technology opportunities in areas such as transportation, communications, commerce, entertainment and healthcare. AI, the most important technology of our time, is turbocharging innovation across every facet of society. It's expected to generate trillions of dollars in economic dividends and productivity gains. Countries are investing in sovereign AI to develop and harness such benefits on their own. Sovereign AI refers to a nation's capabilities to produce artificial intelligence using its own infrastructure, data, workforce and business networks.

Why Sovereign AI Is Important
The global imperative for nations to invest in sovereign AI capabilities has grown since the rise of generative AI, which is reshaping markets, challenging governance models, inspiring new industries and transforming others—from gaming to biopharma. It's also rewriting the nature of work, as people in many fields start using AI-powered "copilots." Sovereign AI encompasses both physical and data infrastructures. The latter includes sovereign foundation models, such as large language models, developed by local teams and trained on local datasets to promote inclusiveness with specific dialects, cultures and practices. For example, speech AI models can help preserve, promote and revitalize indigenous languages. And LLMs aren't just for teaching AIs human languages, but for writing software code, protecting consumers from financial fraud, teaching robots physical skills and much more.

HP Inc. today announced the industry's largest portfolio of AI PCs leveraging the power of AI to enhance productivity, creativity, and user experiences in hybrid work settings.

In an ever-changing hybrid work landscape, workers are still struggling with disconnection and digital fatigue. HP's 2023 Work Relationship Index reveals that only 27% of knowledge workers have a healthy relationship with work, and 83% believe it's time to redefine our relationships with work. Most employees believe AI will open new opportunities to enjoy work and make their jobs easier, but they need the right AI tools and technology to succeed.

NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago. For the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.

For fiscal 2024, revenue was up 126% to $60.9 billion. GAAP earnings per diluted share was $11.93, up 586% from a year ago. Non-GAAP earnings per diluted share was $12.96, up 288% from a year ago. "Accelerated computing and generative AI have hit the tipping point. Demand is surging worldwide across companies, industries and nations," said Jensen Huang, founder and CEO of NVIDIA.

Providing a peek at the architecture powering advanced AI factories, NVIDIA released a video that offers the first public look at Eos, its latest data-center-scale supercomputer. An extremely large-scale NVIDIA DGX SuperPOD, Eos is where NVIDIA developers create their AI breakthroughs using accelerated computing infrastructure and fully optimized software. Eos is built with 576 NVIDIA DGX H100 systems, NVIDIA Quantum-2 InfiniBand networking and software, providing a total of 18.4 exaflops of FP8 AI performance. Revealed in November at the Supercomputing 2023 trade show, Eos—named for the Greek goddess said to open the gates of dawn each day—reflects NVIDIA's commitment to advancing AI technology.

Eos Supercomputer Fuels Innovation
Each DGX H100 system is equipped with eight NVIDIA H100 Tensor Core GPUs. Eos features a total of 4,608 H100 GPUs. As a result, Eos can handle the largest AI workloads to train large language models, recommender systems, quantum simulations and more. It's a showcase of what NVIDIA's technologies can do, when working at scale. Eos is arriving at the perfect time. People are changing the world with generative AI, from drug discovery to chatbots to autonomous machines and beyond. To achieve these breakthroughs, they need more than AI expertise and development skills. They need an AI factory—a purpose-built AI engine that's always available and can help ramp their capacity to build AI models at scale Eos delivers. Ranked No. 9 in the TOP 500 list of the world's fastest supercomputers, Eos pushes the boundaries of AI technology and infrastructure.

In a major stride toward building a shared national research infrastructure, the U.S. National Science Foundation has launched the National Artificial Intelligence Research Resource pilot program with significant support from NVIDIA. The initiative aims to broaden access to the tools needed to power responsible AI discovery and innovation. It was announced Wednesday in partnership with 10 other federal agencies as well as private-sector, nonprofit and philanthropic organizations. "The breadth of partners that have come together for this pilot underscores the urgency of developing a National AI Research Resource for the future of AI in America," said NSF Director Sethuraman Panchanathan. "By investing in AI research through the NAIRR pilot, the United States unleashes discovery and impact and bolsters its global competitiveness."

NVIDIA's commitment of $30 million in technology contributions over two years is a key factor in enlarging the scale of the pilot, fueling the potential for broader achievements and accelerating the momentum toward full-scale implementation. "The NAIRR is a vision of a national research infrastructure that will provide access to computing, data, models and software to empower researchers and communities," said Katie Antypas, director of the Office of Advanced Cyberinfrastructure at the NSF. "Our primary goals for the NAIRR pilot are to support fundamental AI research and domain-specific research applying AI, reach broader communities, particularly those currently unable to participate in the AI innovation ecosystem, and refine the design for the future full NAIRR," Antypas added.

"This year, every industry will become a technology industry," NVIDIA founder and CEO Jensen Huang told attendees last Wednesday during the annual J.P. Morgan Healthcare Conference. "You can now recognize and learn the language of almost anything with structure, and you can translate it to anything with structure—so text-protein, protein-text," Huang said in a fireside chat with Martin Chavez, partner and vice chairman of global investment firm Sixth Street Partners and board chair of Recursion, a biopharmaceutical company. "This is the generative AI revolution."

The conversation, which took place at the historic San Francisco Mint, followed a presentation at the J.P. Morgan conference Monday by Kimberly Powell, NVIDIA's VP of healthcare. In her talk, Powell announced that Recursion is the first hosting partner to offer a foundation model through the NVIDIA BioNeMo cloud service, which is advancing into beta this month. She also said that Amgen, one of the first companies to employ BioNeMo, plans to advance drug discovery with generative AI and NVIDIA DGX SuperPOD—and that BioNeMo is used by a growing number of techbio companies, pharmas, AI software vendors and systems integrators. Among them are Deloitte, Innophore, Insilico Medicine, OneAngstrom, Recursion and Terray Therapeutics.

Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and NVIDIA (NASDAQ: NVDA) today announced an expansion of their strategic collaboration to deliver the most-advanced infrastructure, software and services to power customers' generative artificial intelligence (AI) innovations. The companies will bring together the best of NVIDIA and AWS technologies—from NVIDIA's newest multi-node systems featuring next-generation GPUs, CPUs and AI software, to AWS Nitro System advanced virtualization and security, Elastic Fabric Adapter (EFA) interconnect, and UltraCluster scalability—that are ideal for training foundation models and building generative AI applications.

The expanded collaboration builds on a longstanding relationship that has fueled the generative AI era by offering early machine learning (ML) pioneers the compute performance required to advance the state-of-the-art in these technologies.

NVIDIA today introduced an AI foundry service to supercharge the development and tuning of custom generative AI applications for enterprises and startups deploying on Microsoft Azure.

The NVIDIA AI foundry service pulls together three elements—a collection of NVIDIA AI Foundation Models, NVIDIA NeMo framework and tools, and NVIDIA DGX Cloud AI supercomputing services—that give enterprises an end-to-end solution for creating custom generative AI models. Businesses can then deploy their customized models with NVIDIA AI Enterprise software to power generative AI applications, including intelligent search, summarization and content generation.

The 62nd edition of the TOP500 reveals that the Frontier system retains its top spot and is still the only exascale machine on the list. However, five new or upgraded systems have shaken up the Top 10.

Housed at the Oak Ridge National Laboratory (ORNL) in Tennessee, USA, Frontier leads the pack with an HPL score of 1.194 EFlop/s - unchanged from the June 2023 list. Frontier utilizes AMD EPYC 64C 2GHz processors and is based on the latest HPE Cray EX235a architecture. The system has a total of 8,699,904 combined CPU and GPU cores. Additionally, Frontier has an impressive power efficiency rating of 52.59 GFlops/watt and relies on HPE's Slingshot 11 network for data transfer.

The recent development of AI models that are more capable than ever has led to a massive demand for hardware infrastructure that powers them. As the dominant player in the industry with its GPU and CPU-GPU solutions, NVIDIA has reportedly discussed leasing data center space to power its own cloud service for these AI applications. Called NVIDIA Cloud DGX, it will reportedly put the company right up against its clients, which are cloud service providers (CSPs) as well. Companies like Microsoft Azure, Amazon AWS, Google Cloud, and Oracle actively acquire NVIDIA GPUs to power their GPU-accelerated cloud instances. According to the report, this has been developing for a few years.

Additionally, it is worth noting that NVIDIA already owns parts for its potential data center infrastructure. This includes NVIDIA DGX and HGX units, which can just be interconnected in a data center, with cloud provisioning so developers can access NVIDIA's instances. A great benefit that would attract the end-user is that NVIDIA could potentially lower the price point of its offerings, as they are acquiring GPUs for much less compared to the CSPs that receive them with a profit margin that NVIDIA imposes. This can attract potential customers, leaving hyperscalers like Amazon, Microsoft, and Google without a moat in the cloud game. Of course, until this project is official, we should take this information with a grain of salt.

In a major step to support India's industrial sector, NVIDIA and Reliance Industries today announced a collaboration to develop India's own foundation large language model trained on the nation's diverse languages and tailored for generative AI applications to serve the world's most populous nation. The companies will work together to build AI infrastructure that is over an order of magnitude more powerful than the fastest supercomputer in India today. NVIDIA will provide access to the most advanced NVIDIA GH200 Grace Hopper Superchip and NVIDIA DGX Cloud, an AI supercomputing service in the cloud. GH200 marks a fundamental shift in computing architecture that provides exceptional performance and massive memory bandwidth.

The NVIDIA-powered AI infrastructure is the foundation of the new frontier into AI for Reliance Jio Infocomm, Reliance Industries' telecom arm. The global AI revolution is transforming industries and daily life. To serve India's vast potential in AI, Reliance will create AI applications and services for their 450 million Jio customers and provide energy-efficient AI infrastructure to scientists, developers and startups across India.

Google Cloud Next—Google Cloud and NVIDIA today announced new AI infrastructure and software for customers to build and deploy massive models for generative AI and speed data science workloads.

In a fireside chat at Google Cloud Next, Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang discussed how the partnership is bringing end-to-end machine learning services to some of the largest AI customers in the world—including by making it easy to run AI supercomputers with Google Cloud offerings built on NVIDIA technologies. The new hardware and software integrations utilize the same NVIDIA technologies employed over the past two years by Google DeepMind and Google research teams.