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."

At GTC on Monday, Microsoft Corp. and NVIDIA expanded their longstanding collaboration with powerful new integrations that leverage the latest NVIDIA generative AI and Omniverse technologies across Microsoft Azure, Azure AI services, Microsoft Fabric and Microsoft 365.

"Together with NVIDIA, we are making the promise of AI real, helping to drive new benefits and productivity gains for people and organizations everywhere," said Satya Nadella, Chairman and CEO, Microsoft. "From bringing the GB200 Grace Blackwell processor to Azure, to new integrations between DGX Cloud and Microsoft Fabric, the announcements we are making today will ensure customers have the most comprehensive platforms and tools across every layer of the Copilot stack, from silicon to software, to build their own breakthrough AI capability."

"AI is transforming our daily lives - opening up a world of new opportunities," said Jensen Huang, founder and CEO of NVIDIA. "Through our collaboration with Microsoft, we're building a future that unlocks the promise of AI for customers, helping them deliver innovative solutions to the world."

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.

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.

The spirit of software pioneer Grace Hopper will live on at NVIDIA GTC. Accelerated systems using powerful processors - named in honor of the pioneer of software programming - will be on display at the global AI conference running March 18-21, ready to take computing to the next level. System makers will show more than 500 servers in multiple configurations across 18 racks, all packing NVIDIA GH200 Grace Hopper Superchips. They'll form the largest display at NVIDIA's booth in the San Jose Convention Center, filling the MGX Pavilion.

MGX Speeds Time to Market
NVIDIA MGX is a blueprint for building accelerated servers with any combination of GPUs, CPUs and data processing units (DPUs) for a wide range of AI, high performance computing and NVIDIA Omniverse applications. It's a modular reference architecture for use across multiple product generations and workloads. GTC attendees can get an up-close look at MGX models tailored for enterprise, cloud and telco-edge uses, such as generative AI inference, recommenders and data analytics. The pavilion will showcase accelerated systems packing single and dual GH200 Superchips in 1U and 2U chassis, linked via NVIDIA BlueField-3 DPUs and NVIDIA Quantum-2 400 Gb/s InfiniBand networks over LinkX cables and transceivers. The systems support industry standards for 19- and 21-inch rack enclosures, and many provide E1.S bays for nonvolatile storage.

A collaboration including Intel, Dell Technologies, Nvidia and the Ohio Supercomputer Center (OSC), today introduces Cardinal, a cutting-edge high-performance computing (HPC) cluster. Purpose-built to meet the increasing demand for HPC resources in Ohio across research, education and industry innovation, particularly in artificial intelligence (AI).

AI and machine learning are integral tools in scientific, engineering and biomedical fields for solving complex research inquiries. As these technologies continue to demonstrate efficacy, academic domains such as agricultural sciences, architecture and social studies are embracing their potential. Cardinal is equipped with the hardware capable of meeting the demands of expanding AI workloads. In both capabilities and capacity, the new cluster will be a substantial upgrade from the system it will replace, the Owens Cluster launched in 2016.

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.

NVIDIA's AI platform raised the bar for AI training and high performance computing in the latest MLPerf industry benchmarks. Among many new records and milestones, one in generative AI stands out: NVIDIA Eos - an AI supercomputer powered by a whopping 10,752 NVIDIA H100 Tensor Core GPUs and NVIDIA Quantum-2 InfiniBand networking - completed a training benchmark based on a GPT-3 model with 175 billion parameters trained on one billion tokens in just 3.9 minutes. That's a nearly 3x gain from 10.9 minutes, the record NVIDIA set when the test was introduced less than six months ago.

The benchmark uses a portion of the full GPT-3 data set behind the popular ChatGPT service that, by extrapolation, Eos could now train in just eight days, 73x faster than a prior state-of-the-art system using 512 A100 GPUs. The acceleration in training time reduces costs, saves energy and speeds time-to-market. It's heavy lifting that makes large language models widely available so every business can adopt them with tools like NVIDIA NeMo, a framework for customizing LLMs. In a new generative AI test ‌this round, 1,024 NVIDIA Hopper architecture GPUs completed a training benchmark based on the Stable Diffusion text-to-image model in 2.5 minutes, setting a high bar on this new workload. By adopting these two tests, MLPerf reinforces its leadership as the industry standard for measuring AI performance, since generative AI is the most transformative technology of our time.

Today, IBM introduced the new IBM Storage Scale System 6000, a cloud-scale global data platform designed to meet today's data intensive and AI workload demands, and the latest offering in the IBM Storage for Data and AI portfolio.

For the seventh consecutive year and counting, IBM is a 2022 Gartner Magic Quadrant for Distributed File Systems and Object Storage Leader, recognized for its vision and execution. The new IBM Storage Scale System 6000 seeks to build on IBM's leadership position with an enhanced high performance parallel file system designed for data intensive use-cases. It provides up to 7M IOPs and up to 256 GB/s throughput for read only workloads per system in a 4U (four rack units) footprint.

Supermicro, Inc., a Total IT Solution manufacturer for AI, Cloud, Storage, and 5G/Edge, is announcing one of the industry's broadest portfolios of new GPU systems based on the NVIDIA reference architecture, featuring the latest NVIDIA GH200 Grace Hopper and NVIDIA Grace CPU Superchip. The new modular architecture is designed to standardize AI infrastructure and accelerated computing in compact 1U and 2U form factors while providing ultimate flexibility and expansion ability for current and future GPUs, DPUs, and CPUs. Supermicro's advanced liquid-cooling technology enables very high-density configurations, such as a 1U 2-node configuration with 2 NVIDIA GH200 Grace Hopper Superchips integrated with a high-speed interconnect. Supermicro can deliver thousands of rack-scale AI servers per month from facilities worldwide and ensures Plug-and-Play compatibility.

"Supermicro is a recognized leader in driving today's AI revolution, transforming data centers to deliver the promise of AI to many workloads," said Charles Liang, president and CEO of Supermicro. "It is crucial for us to bring systems that are highly modular, scalable, and universal for rapidly evolving AI technologies. Supermicro's NVIDIA MGX-based solutions show that our building-block strategy enables us to bring the latest systems to market quickly and are the most workload-optimized in the industry. By collaborating with NVIDIA, we are helping accelerate time to market for enterprises to develop new AI-enabled applications, simplifying deployment and reducing environmental impact. The range of new servers incorporates the latest industry technology optimized for AI, including NVIDIA GH200 Grace Hopper Superchips, BlueField, and PCIe 5.0 EDSFF slots."

Microsoft Azure users can now turn to the latest NVIDIA accelerated computing technology to train and deploy their generative AI applications. Available today, the Microsoft Azure ND H100 v5 VMs using NVIDIA H100 Tensor Core GPUs and NVIDIA Quantum-2 InfiniBand networking—enables scaling generative AI, high performance computing (HPC) and other applications with a click from a browser. Available to customers across the U.S., the new instance arrives as developers and researchers are using large language models (LLMs) and accelerated computing to uncover new consumer and business use cases.

The NVIDIA H100 GPU delivers supercomputing-class performance through architectural innovations, including fourth-generation Tensor Cores, a new Transformer Engine for accelerating LLMs and the latest NVLink technology that lets GPUs talk to each other at 900 GB/s. The inclusion of NVIDIA Quantum-2 CX7 InfiniBand with 3,200 Gbps cross-node bandwidth ensures seamless performance across the GPUs at massive scale, matching the capabilities of top-performing supercomputers globally.

Increasing its HPL score from 1.02 Eflop/s in November 2022 to an impressive 1.194 Eflop/s on this list, Frontier was able to improve upon its score after a stagnation between June 2022 and November 2022. Considering exascale was only a goal to aspire to just a few years ago, a roughly 17% increase here is an enormous success. Additionally, Frontier earned a score of 9.95 Eflop/s on the HLP-MxP benchmark, which measures performance for mixed-precision calculation. This is also an increase over the 7.94 EFlop/s that the system achieved on the previous list and nearly 10 times more powerful than the machine's HPL score. Frontier is based on the HPE Cray EX235a architecture and utilizes AMD EPYC 64C 2 GHz processors. It also has 8,699,904 cores and an incredible energy efficiency rating of 52.59 Gflops/watt. It also relies on gigabit ethernet for data transfer.

NVIDIA today announced a supercomputer built on the NVIDIA Grace CPU Superchip, adding to a wave of new energy-efficient supercomputers based on the Arm Neoverse platform. The Isambard 3 supercomputer to be based at the Bristol & Bath Science Park, in the U.K., will feature 384 Arm-based NVIDIA Grace CPU Superchips to power medical and scientific research, and is expected to deliver 6x the performance and energy efficiency of Isambard 2, placing it among Europe's most energy-efficient systems.

It will achieve about 2.7 petaflops of FP64 peak performance and consume less than 270 kilowatts of power, ranking it among the world's three greenest non-accelerated supercomputers. The project is being led by the University of Bristol, as part of the research consortium the GW4 Alliance, together with the universities of Bath, Cardiff and Exeter.

Customers from Japan to Ecuador and Sweden are using NVIDIA DGX H100 systems like AI factories to manufacture intelligence. They're creating services that offer AI-driven insights in finance, healthcare, law, IT and telecom—and working to transform their industries in the process. Among the dozens of use cases, one aims to predict how factory equipment will age, so tomorrow's plants can be more efficient.

Called Green Physics AI, it adds information like an object's CO2 footprint, age and energy consumption to SORDI.ai, which claims to be the largest synthetic dataset in manufacturing.

Microsoft Azure announced their new ND H100 v5 virtual machine which packs Intel's Sapphire Rapids Xeon Scalable processors with NVIDIA's Hopper H100 GPUs, as well as NVIDIA's Quantum-2 CX7 interconnect. Inside each physical machine sits eight H100s—presumably the SXM5 variant packing a whopping 132 SMs and 528 4th generation tensor cores—interconnected by NVLink 4.0 which ties them all together with 3.6 TB/s bisectional bandwidth. Outside each local machine is a network of thousands more H100s connected together with 400 GB/s Quantum-2 CX7 InfiniBand, which Microsoft says allows 3.2 Tb/s per VM for on-demand scaling to accelerate the largest AI training workloads.

Generative AI solutions like ChatGPT have accelerated demand for multi-ExaOP cloud services that can handle the large training sets and utilize the latest development tools. Azure's new ND H100 v5 VMs offer that capability to organizations of any size, whether you're a smaller startup or a larger company looking to implement large-scale AI training deployments. While Microsoft is not making any direct claims for performance, NVIDIA has advertised H100 as running up to 30x faster than the preceding Ampere architecture that is currently offered with the ND A100 v4 VMs.

The 60th edition of the TOP500 reveals that the Frontier system is still the only true exascale machine on the list.

With an HPL score of 1.102 EFlop/s, the Frontier machine at Oak Ridge National Laboratory (ORNL) did not improve upon the score it reached on the June 2022 list. That said, Frontier's near-tripling of the HPL score received by second-place winner is still a major victory for computer science. On top of that, Frontier demonstrated a score of 7.94 EFlop/s on the HPL-MxP benchmark, which measures performance for mixed-precision calculation. Frontier is based on the HPE Cray EX235a architecture and it relies on AMD EPYC 64C 2 GHz processor. The system has 8,730,112 cores and a power efficiency rating of 52.23 gigaflops/watt. It also relies on gigabit ethernet for data transfer.

Fujitsu has successfully developed the world's fastest quantum computer simulator capable of handling 36 qubit quantum circuits on a cluster system featuring Fujitsu's "FUJITSU Supercomputer PRIMEHPC FX 700" ("PRIMEHPC FX 700")(1), which is equipped with the same A64FX CPU that powers the world's fastest supercomputer, Fugaku.

The newly developed quantum simulator can execute the quantum simulator software "Qulacs"(3) in parallel at high speed, achieving approximately double the performance of other significant quantum simulators in 36 qubit quantum operations. Fujitsu's new quantum simulator will serve as an important bridge towards the development of quantum computing applications that are expected to be put to practical use in the years ahead.

Excelero, a Tel Aviv-based provider of high-performance software-defined storage, is now a part of NVIDIA. The company's team of engineers—including its seasoned co-founders with decades of experience in HPC, storage and networking—bring deep expertise in the block storage that large businesses use in storage-area networks.

Now their mission is to help expand support for block storage in our enterprise software stack such as clusters for high performance computing. Block storage also has an important role to play inside the DOCA software framework that runs on our DPUs.

NVIDIA today reported record revenue for the third quarter ended October 31, 2021, of $7.10 billion, up 50 percent from a year earlier and up 9 percent from the previous quarter, with record revenue from the company's Gaming, Data Center and Professional Visualization market platforms. GAAP earnings per diluted share for the quarter were $0.97, up 83 percent from a year ago and up 3 percent from the previous quarter. Non-GAAP earnings per diluted share were $1.17, up 60 percent from a year ago and up 13 percent from the previous quarter.

"The third quarter was outstanding, with record revenue," said Jensen Huang, founder and CEO of NVIDIA. "Demand for NVIDIA AI is surging, driven by hyperscale and cloud scale-out, and broadening adoption by more than 25,000 companies. NVIDIA RTX has reinvented computer graphics with ray tracing and AI, and is the ideal upgrade for the large, growing market of gamers and creators, as well as designers and professionals building home workstations.

Xilinx, Inc., the leader in adaptive computing, today at the SC21 supercomputing conference introduced the Alveo U55C data center accelerator card and a new standards-based, API-driven clustering solution for deploying FPGAs at massive scale. The Alveo U55C accelerator brings superior performance-per-watt to high performance computing (HPC) and database workloads and easily scales through the Xilinx HPC clustering solution.

Purpose-built for HPC and big data workloads, the new Alveo U55C card is the company's most powerful Alveo accelerator card ever, offering the highest compute density and HBM capacity in the Alveo accelerator portfolio. Together with the new Xilinx RoCE v2-based clustering solution, a broad spectrum of customers with large-scale compute workloads can now implement powerful FPGA-based HPC clustering using their existing data center infrastructure and network.

The 58th annual edition of the TOP500 saw little change in the Top10. The Microsoft Azure system called Voyager-EUS2 was the only machine to shake up the top spots, claiming No. 10. Based on an AMD EPYC processor with 48 cores and 2.45GHz working together with an NVIDIA A100 GPU and 80 GB of memory, Voyager-EUS2 also utilizes a Mellanox HDR Infiniband for data transfer.

While there were no other changes to the positions of the systems in the Top10, Perlmutter at NERSC improved its performance to 70.9 Pflop/s. Housed at the Lawrence Berkeley National Laboratory, Perlmutter's increased performance couldn't move it from its previously held No. 5 spot.

NVIDIA today announced NVIDIA Quantum-2, the next generation of its InfiniBand networking platform, which offers the extreme performance, broad accessibility and strong security needed by cloud computing providers and supercomputing centers.

The most advanced end-to-end networking platform ever built, NVIDIA Quantum-2 is a 400 Gbps InfiniBand networking platform that consists of the NVIDIA Quantum-2 switch, the ConnectX-7 network adapter, the BlueField-3 data processing unit (DPU) and all the software that supports the new architecture.

NVIDIA today officially launched Cambridge-1, the United Kingdom's most powerful supercomputer, which will enable top scientists and healthcare experts to use the powerful combination of AI and simulation to accelerate the digital biology revolution and bolster the country's world-leading life sciences industry. Dedicated to advancing healthcare, Cambridge-1 represents a $100 million investment by NVIDIA. Its first projects with AstraZeneca, GSK, Guy's and St Thomas' NHS Foundation Trust, King's College London and Oxford Nanopore Technologies include developing a deeper understanding of brain diseases like dementia, using AI to design new drugs and improving the accuracy of finding disease-causing variations in human genomes.

Cambridge-1 brings together decades of NVIDIA's work in accelerated computing, AI and life sciences, where NVIDIA Clara and AI frameworks are optimized to take advantage of the entire system for large-scale research. An NVIDIA DGX SuperPOD supercomputing cluster, it ranks among the world's top 50 fastest computers and is powered by 100 percent renewable energy.

At the 2021 International Supercomputing Conference (ISC) Intel is showcasing how the company is extending its lead in high performance computing (HPC) with a range of technology disclosures, partnerships and customer adoptions. Intel processors are the most widely deployed compute architecture in the world's supercomputers, enabling global medical discoveries and scientific breakthroughs. Intel is announcing advances in its Xeon processor for HPC and AI as well as innovations in memory, software, exascale-class storage, and networking technologies for a range of HPC use cases.

"To maximize HPC performance we must leverage all the computer resources and technology advancements available to us," said Trish Damkroger, vice president and general manager of High Performance Computing at Intel. "Intel is the driving force behind the industry's move toward exascale computing, and the advancements we're delivering with our CPUs, XPUs, oneAPI Toolkits, exascale-class DAOS storage, and high-speed networking are pushing us closer toward that realization."