Mitsui & Co., Ltd., one of Japan's largest business conglomerates, is collaborating with NVIDIA on Tokyo-1—an initiative to supercharge the nation's pharmaceutical leaders with technology, including high-resolution molecular dynamics simulations and generative AI models for drug discovery.

Announced today at the NVIDIA GTC global AI conference, the Tokyo-1 project features an NVIDIA DGX AI supercomputer that will be accessible to Japan's pharma companies and startups. The effort is poised to accelerate Japan's $100 billion pharma industry, the world's third largest following the U.S. and China.

NVIDIA and key partners today announced the availability of new products and services featuring the NVIDIA H100 Tensor Core GPU—the world's most powerful GPU for AI—to address rapidly growing demand for generative AI training and inference. Oracle Cloud Infrastructure (OCI) 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 Amazon EC2 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.

Additionally, Meta has now deployed its H100-powered Grand Teton AI supercomputer internally for its AI production and research teams. NVIDIA founder and CEO Jensen Huang announced during his GTC keynote today that NVIDIA DGX H100 AI supercomputers are in full production and will be coming soon to enterprises worldwide.

NVIDIA today announced a breakthrough that brings accelerated computing to the field of computational lithography, enabling semiconductor leaders like ASML, TSMC and Synopsys to accelerate the design and manufacturing of next-generation chips, just as current production processes are nearing the limits of what physics makes possible.

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. Equipment maker ASML is working closely with NVIDIA on GPUs and cuLitho, and is planning to integrate support for GPUs into all of its computational lithography software products.

NVIDIA today announced a new system built with Quantum Machines that provides a revolutionary new architecture for researchers working in high-performance and low-latency quantum-classical computing. The world's first GPU-accelerated quantum computing system, the NVIDIA DGX Quantum brings together the world's most powerful accelerated computing platform - enabled by the NVIDIA Grace Hopper Superchip and CUDA Quantum open-source programming model - with the world's most advanced quantum control platform, OPX, by Quantum Machines.

The combination allows researchers to build extraordinarily powerful applications that combine quantum computing with state-of-the-art classical computing, enabling calibration, control, quantum error correction and hybrid algorithms. "Quantum-accelerated supercomputing has the potential to reshape science and industry with capabilities that can serve humanity in enormous ways," said Tim Costa, director of HPC and quantum at NVIDIA. "NVIDIA DGX Quantum will enable researchers to push the boundaries of quantum-classical computing."

NVIDIA has recently reported its Q4 earnings, and the earnings call following the report contains exciting details about the company and its plans to open up to new possibilities. NVIDIA's CEO Jensen Huang has stated that the company is on track to become an AI-as-a-Service (AIaaS) provider, which technically makes it a cloud service provider (CSP). "Today, I want to share with you the next level of our business model to help put AI within reach of every enterprise customer. We are partnering with major service -- cloud service providers to offer NVIDIA AI cloud services, offered directly by NVIDIA and through our network of go-to-market partners, and hosted within the world's largest clouds." Said Mr. Huang, adding that "NVIDIA AI as a service offers enterprises easy access to the world's most advanced AI platform, while remaining close to the storage, networking, security and cloud services offered by the world's most advanced clouds. Customers can engage NVIDIA AI cloud services at the AI supercomputer, acceleration library software, or pretrained AI model layers."

In addition to enrolling other CSPs into the race, NVIDIA is also going to offer DGX machines on demand in the cloud. Using select CSPs, you can get access to an entire DGX and harness the computing power for AI research purposes. Mr. Huang noted "NVIDIA DGX is an AI supercomputer, and the blueprint of AI factories being built around the world. AI supercomputers are hard and time-consuming to build. Today, we are announcing the NVIDIA DGX Cloud, the fastest and easiest way to have your own DGX AI supercomputer, just open your browser. NVIDIA DGX Cloud is already available through Oracle Cloud Infrastructure and Microsoft Azure, Google GCP, and others on the way."

AI is at the heart of humanity's most transformative innovations—from developing COVID vaccines at unprecedented speeds and diagnosing cancer to powering autonomous vehicles and understanding climate change. Virtually every industry will benefit from adopting AI, but the technology has become more resource intensive as neural networks have increased in complexity. To avoid placing unsustainable demands on electricity generation to run this computing infrastructure, the underlying technology must be as efficient as possible.

Accelerated computing powered by NVIDIA GPUs and the NVIDIA AI platform offer the efficiency that enables data centers to sustainably drive the next generation of breakthroughs. And now, timed with the launch of 4th Gen Intel Xeon Scalable processors, NVIDIA and its partners have kicked off a new generation of accelerated computing systems that are built for energy-efficient AI. When combined with NVIDIA H100 Tensor Core GPUs, these systems can deliver dramatically higher performance, greater scale and higher efficiency than the prior generation, providing more computation and problem-solving per watt.

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.

Rescale, the leader in high performance computing built for the cloud to accelerate engineering innovation, today announced it is teaming with NVIDIA to integrate the NVIDIA AI platform into Rescale's HPC-as-a-Service offering. The integration is designed to advance computational engineering simulation with AI and machine learning, helping enterprises commercialize new product innovations faster, more efficiently and at less cost.

Additionally, Rescale announced the world's first Compute Recommendation Engine (CRE) to power Intelligent Computing for HPC and AI workloads. Optimizing workload performance can be prohibitively complex as organizations seek to balance decisions among architectures, geographic regions, price points, scalability, service levels, compliance, and sustainability objectives. Developed using machine learning on NVIDIA architectures with infrastructure telemetry, industry benchmarks, and full-stack metadata spanning over 100 million production HPC workloads, Rescale CRE provides customers unprecedented insight to optimize overall performance.

ASUS, the leading IT company in server systems, server motherboards and workstations, today announced its presence at NVIDIA GTC - a developer conference for the era of AI and the metaverse. ASUS will focus on three demonstrations outlining its strategic developments in AI, including: the methodology behind ASUS MLPerf Training v2.0 results that achieved multiple breakthrough records; a success story exploring the building of an academic AI data center at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia; and a research AI data center created in conjunction with the National Health Research Institute in Taiwan.

MLPerf benchmark results help advance machine-learning performance and efficiency, allowing researchers to evaluate the efficacy of AI training and inference based on specific server configurations. Since joining MLCommons in 2021, ASUS has gained multiple breakthrough records in the data center closed division across six AI-benchmark tasks in AI training and inferencing MLPerf Training v2.0. At the ASUS GTC session, senior ASUS software engineers will share the methodology for achieving these world-class results—as well as the company's efforts to deliver more efficient AI workflows through machine learning.

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 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.

NVIDIA (NASDAQ: NVDA) today reported record revenue for the first quarter ended May 2, 2021, of $5.66 billion, up 84 percent from a year earlier and up 13 percent from the previous quarter, with record revenue from the company's Gaming, Data Center and Professional Visualization platforms. GAAP earnings per diluted share for the quarter were a record $3.03, up 106 percent from a year ago and up 31 percent from the previous quarter. Non-GAAP earnings per diluted share were $3.66, up 103 percent from a year earlier and up 18 percent from the previous quarter.

"We had a fantastic quarter, with strong demand for our products driving record revenue," said Jensen Huang, founder and CEO of NVIDIA. "Our Data Center business continues to expand, as the world's industries take up NVIDIA AI to process computer vision, conversational AI, natural language understanding and recommender systems. NVIDIA RTX has reinvented computer graphics and is driving upgrades across the gaming and design markets. Our partners are launching the largest-ever wave of NVIDIA-powered laptops. Across industries, the adoption of NVIDIA computing platforms is accelerating.

NVIDIA today announced its first data center CPU, an Arm-based processor that will deliver 10x the performance of today's fastest servers on the most complex AI and high performance computing workloads.

The result of more than 10,000 engineering years of work, the NVIDIA Grace CPU is designed to address the computing requirements for the world's most advanced applications—including natural language processing, recommender systems and AI supercomputing—that analyze enormous datasets requiring both ultra-fast compute performance and massive memory. It combines energy-efficient Arm CPU cores with an innovative low-power memory subsystem to deliver high performance with great efficiency.

NVIDIA today unveiled the world's first cloud-native, multi-tenant AI supercomputer—the next-generation NVIDIA DGX SuperPOD featuring NVIDIA BlueField -2 DPUs. Fortifying the DGX SuperPOD with BlueField-2 DPUs—data processing units that offload, accelerate and isolate users' data—provides customers with secure connections to their AI infrastructure.

The company also announced NVIDIA Base Command, which enables multiple users and IT teams to securely access, share and operate their DGX SuperPOD infrastructure. Base Command coordinates AI training and operations on DGX SuperPOD infrastructure to enable the work of teams of data scientists and developers located around the globe.

The 56th edition of the TOP500 saw the Japanese Fugaku supercomputer solidify its number one status in a list that reflects a flattening performance growth curve. Although two new systems managed to make it into the top 10, the full list recorded the smallest number of new entries since the project began in 1993.

The entry level to the list moved up to 1.32 petaflops on the High Performance Linpack (HPL) benchmark, a small increase from 1.23 petaflops recorded in the June 2020 rankings. In a similar vein, the aggregate performance of all 500 systems grew from 2.22 exaflops in June to just 2.43 exaflops on the latest list. Likewise, average concurrency per system barely increased at all, growing from 145,363 cores six months ago to 145,465 cores in the current list.

NVIDIA today unveiled the NVIDIA A100 80 GB GPU—the latest innovation powering the NVIDIA HGX AI supercomputing platform—with twice the memory of its predecessor, providing researchers and engineers unprecedented speed and performance to unlock the next wave of AI and scientific breakthroughs. The new A100 with HBM2E technology doubles the A100 40 GB GPU's high-bandwidth memory to 80 GB and delivers over 2 terabytes per second of memory bandwidth. This allows data to be fed quickly to A100, the world's fastest data center GPU, enabling researchers to accelerate their applications even faster and take on even larger models and datasets.

"Achieving state-of-the-art results in HPC and AI research requires building the biggest models, but these demand more memory capacity and bandwidth than ever before," said Bryan Catanzaro, vice president of applied deep learning research at NVIDIA. "The A100 80 GB GPU provides double the memory of its predecessor, which was introduced just six months ago, and breaks the 2 TB per second barrier, enabling researchers to tackle the world's most important scientific and big data challenges."

NVIDIA today announced that it is building the United Kingdom's most powerful supercomputer, which it will make available to U.K. healthcare researchers using AI to solve pressing medical challenges, including those presented by COVID-19.

Expected to come online by year end, the "Cambridge-1" supercomputer will be an NVIDIA DGX SuperPOD system capable of delivering more than 400 petaflops of AI performance and 8 petaflops of Linpack performance, which would rank it No. 29 on the latest TOP500 list of the world's most powerful supercomputers. It will also rank among the world's top 3 most energy-efficient supercomputers on the current Green500 list.

NVIDIA today reported record revenue for the second quarter ended July 26, 2020, of $3.87 billion, up 50 percent from $2.58 billion a year earlier, and up 26 percent from $3.08 billion in the previous quarter.

GAAP earnings per diluted share for the quarter were $0.99, up 10 percent from $0.90 a year ago, and down 33 percent from $1.47 in the previous quarter. Non-GAAP earnings per diluted share were $2.18, up 76 percent from $1.24 a year earlier, and up 21 percent from $1.80 in the previous quarter. NVIDIA closed its acquisition of Mellanox Technologies Ltd. on April 27, 2020. "Adoption of NVIDIA computing is accelerating, driving record revenue and exceptional growth," said Jensen Huang, founder and CEO of NVIDIA. "Growth in GeForce gaming accelerated as gamers increasingly immerse themselves in realistic virtual worlds created by NVIDIA RTX ray tracing and AI.

The University of Florida and NVIDIA Tuesday unveiled a plan to build the world's fastest AI supercomputer in academia, delivering 700 petaflops of AI performance. The effort is anchored by a $50 million gift: $25 million from alumnus and NVIDIA co-founder Chris Malachowsky and $25 million in hardware, software, training and services from NVIDIA.

"We've created a replicable, powerful model of public-private cooperation for everyone's benefit," said Malachowsky, who serves as an NVIDIA Fellow, in an online event featuring leaders from both the UF and NVIDIA. UF will invest an additional $20 million to create an AI-centric supercomputing and data center.

AMD today announced the NVIDIA DGX A100, the third generation of the world's most advanced AI system, is the latest high-performance computing system featuring 2nd Gen AMD EPYC processors. Delivering 5 petaflops of AI performance, the elastic architecture of the NVIDIA DGX A100 enables enterprises to accelerate diverse AI workloads such as data analytics, training, and inference.

NVIDIA DGX A100 leverages the high-performance capabilities, 128 cores, DDR4-3200 MHz and PCIe 4 support from two AMD EPYC 7742 processors running at speeds up to 3.4 GHz¹. The 2nd Gen AMD EPYC processor is the first and only current x86-architecture server processor that supports PCIe 4, providing leadership high-bandwidth I/O that's critical for high performance computing and connections between the CPU and other devices like GPUs.

NVIDIA today reported revenue for the first quarter ended April 26, 2020, of $3.08 billion, up 39 percent from $2.22 billion a year earlier, and down 1 percent from $3.11 billion in the previous quarter. GAAP earnings per diluted share for the quarter were $1.47, up 130 percent from $0.64 a year ago, and down 4 percent from $1.53 in the previous quarter. Non-GAAP earnings per diluted share were $1.80, up 105 percent from $0.88 a year earlier, and down 5 percent from $1.89 in the previous quarter.

NVIDIA completed its acquisition of Mellanox Technologies Ltd. on April 27, 2020, for a transaction value of $7 billion. It also transitioned its GPU Technology Conference to an all-digital format, drawing more than 55,000 registered participants, while NVIDIA founder and CEO Jensen Huang's keynote videos were viewed 3.8 million times in their first three days.

NVIDIA today unveiled NVIDIA DGX A100, the third generation of the world's most advanced AI system, delivering 5 petaflops of AI performance and consolidating the power and capabilities of an entire data center into a single flexible platform for the first time. Immediately available, DGX A100 systems have begun shipping worldwide, with the first order going to the U.S. Department of Energy's (DOE) Argonne National Laboratory, which will use the cluster's AI and computing power to better understand and fight COVID-19.

"NVIDIA DGX A100 is the ultimate instrument for advancing AI," said Jensen Huang, founder and CEO of NVIDIA. "NVIDIA DGX is the first AI system built for the end-to-end machine learning workflow - from data analytics to training to inference. And with the giant performance leap of the new DGX, machine learning engineers can stay ahead of the exponentially growing size of AI models and data."

NVIDIA is rumored to introduce their next-generation Ampere architecture very soon, at its GTC event happening on May 14th. We're expecting to see an announcement for the successor to the company's DGX lineup of pre-built compute systems—using the upcoming Ampere architecture of course. At the heart of these machines, will be a new GA100 GPU, that's rumored to be very fast. A while ago, we've seen NVIDIA register a trademark for "DGX A100", which seems to be a credible name for these systems featuring the new Tesla A100 graphics cards.

Today, NVIDIA's CEO was spotted in an unlisted video that's published on the official NVIDIA YouTube channel. It shows him pulling out of the oven what he calls "world's largest GPU", that he has been cooking all the time. Featuring eight Tesla A100 GPUs, this DGX A100 system appears to be based on a similar platform design as previous DGX systems, where the GPU is a socketed SXM2 design. This looks like a viable upgrade path for owners of previous DGX systems—just swap out the GPUs and enjoy higher performance. It's been a while since we have seen Mr. Huang appear with his leather jacket, and in the video, he isn't wearing one, is this the real Jensen? Jokes aside, you can check out the video below, if it is not taken down soon.Update May 12th, 5 pm UTC: NVIDIA has listed the video and it is not unlisted anymore.