Cerebras Systems, the pioneer in accelerating generative AI, and G42, the Abu Dhabi-based leading technology holding group, today announced the build of Condor Galaxy 3 (CG-3), the third cluster of their constellation of AI supercomputers, the Condor Galaxy. Featuring 64 of Cerebras' newly announced CS-3 systems - all powered by the industry's fastest AI chip, the Wafer-Scale Engine 3 (WSE-3) - Condor Galaxy 3 will deliver 8 exaFLOPs of AI with 58 million AI-optimized cores. The Cerebras and G42 strategic partnership already delivered 8 exaFLOPs of AI supercomputing performance via Condor Galaxy 1 and Condor Galaxy 2, each amongst the largest AI supercomputers in the world. Located in Dallas, Texas, Condor Galaxy 3 brings the current total of the Condor Galaxy network to 16 exaFLOPs.

"With Condor Galaxy 3, we continue to achieve our joint vision of transforming the worldwide inventory of AI compute through the development of the world's largest and fastest AI supercomputers," said Kiril Evtimov, Group CTO of G42. "The existing Condor Galaxy network has trained some of the leading open-source models in the industry, with tens of thousands of downloads. By doubling the capacity to 16exaFLOPs, we look forward to seeing the next wave of innovation Condor Galaxy supercomputers can enable." At the heart of Condor Galaxy 3 are 64 Cerebras CS-3 Systems. Each CS-3 is powered by the new 4 trillion transistor, 900,000 AI core WSE-3. Manufactured at TSMC at the 5-nanometer node, the WSE-3 delivers twice the performance at the same power and for the same price as the previous generation part. Purpose built for training the industry's largest AI models, WSE-3 delivers an astounding 125 petaflops of peak AI performance per chip.

The HPC research projects DEEP-SEA, IO-SEA and RED-SEA are wrapping up this month after a three-year project term. The three projects worked together to develop key technologies for European Exascale supercomputers, based on the Modular Supercomputing Architecture (MSA), a blueprint architecture for highly efficient and scalable heterogeneous Exascale HPC systems. To achieve this, the three projects collaborated on system software and programming environments, data management and storage, as well as interconnects adapted to this architecture. The results of their joint work will be presented at a co-design workshop and poster session at the EuroHPC Summit (Antwerp, 18-21 March, www.eurohpcsummit.eu).

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.

GIGABYTE Technology, an IT pioneer whose focus is to advance global industries through cloud and AI computing systems, is coming to MWC 2024 with its next-generation servers empowering telcos, cloud service providers, enterprises, and SMBs to swiftly harness the value of 5G and AI. Featured is a cutting-edge AI server boasting AMD Instinct MI300X 8-GPU, and a comprehensive AI/HPC server series supporting the latest chip technology from AMD, Intel, and NVIDIA. The showcase will also feature integrated green computing solutions excelling in heat dissipation and energy reduction.

Continuing the booth theme "Future of COMPUTING", GIGABYTE's presentation will cover servers for AI/HPC, RAN and Core networks, modular edge platforms, all-in-one green computing solutions, and AI-powered self-driving technology. The exhibits will demonstrate how industries extend AI applications from cloud to edge and terminal devices through 5G connectivity, expanding future opportunities with faster time to market and sustainable operations. The showcase spans from February 26th to 29th at Booth #5F60, Hall 5, Fira Gran Via, Barcelona.

China's National Supercomputer Center (NUDT) introduced their Tianhe-3 system as a prototype back in early 2019—at the time it had been tested by thirty local organizations. Notable assessors included the Chinese Academy of Sciences and the China Aerodynamics Research and Development Center. The (previous generation) Tianhe-2 system currently sits in a number seven position of world-ranked Supercomputers—offering a measured performance of 33.86 petaFLOPS/s. The internal makeup of its fully formed successor has remained a mystery...until now. The Next Platform believes that the "Xingyi" monikered third generation supercomputer houses the Guangzhou-based lab's MT-3000 processor design. Author, Timothy Prickett Morgan, boasted about acquiring exclusive inside knowledge ahead of international intelligence agencies—many will be keeping an eye on the NUDT, since it is administered by the National University of Defence Technology (itself owned by the Chinese government).

The Next Platform has a track record of outing intimate details relating to Chinese-developed scientific breakthroughs—the semi-related "Oceanlight" system installed at their National Supercomputer Center (Wuxi) was "figured out" two years ago. Tianhe-3 and Oceanlight face significant competition in the form of "El Capitan"—this is the USA's prime: "supercomputer being built right now at Lawrence Livermore National Laboratory by Hewlett Packard Enterprise in conjunction with compute engine supplier AMD. We need to know because we want to understand the very different—and yet, in some ways similar—architectural path that China seems to have taken with the Xingyi architecture to break through the exascale barrier."

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.

Dell Technologies, Intel and the University of Cambridge announce the deployment of the co-designed Dawn Phase 1 supercomputer. Leading technical teams built the U.K.'s fastest AI supercomputer that harnesses the power of both artificial intelligence (AI) and high performance computing (HPC) to solve some of the world's most pressing challenges. This sets a clear way forward for future U.K. technology leadership and inward investment into the U.K. technology sector. Dawn kickstarts the recently launched U.K. AI Research Resource (AIRR), which will explore the viability of associated systems and architectures. Dawn brings the U.K. closer to reaching the compute threshold of a quintillion (1018) floating point operations per second - one exaflop, better known as exascale. For perspective: Every person on earth would have to make calculations 24 hours a day for more than four years to equal a second's worth of processing power in an exascale system.

"Dawn considerably strengthens the scientific and AI compute capability available in the U.K., and it's on the ground, operational today at the Cambridge Open Zettascale Lab. Dell PowerEdge XE9640 servers offer a no-compromises platform to host the Intel Data Center GPU Max Series accelerator, which opens up the ecosystem to choice through oneAPI. I'm very excited to see the sorts of early science this machine can deliver and continue to strengthen the Open Zettascale Lab partnership between Dell Technologies, Intel and the University of Cambridge, and further broaden that to the U.K. scientific and AI community," said Adam Roe, EMEA HPC technical director at Intel.

Tachyum announced that it has accepted a major purchase order from a US company to build a large-scale system, based on its 5 nm Prodigy Universal Processor chip, which delivers more than 50 exaflops performance that will exponentially exceed the computational capabilities of the fastest inference or generative AI supercomputers available anywhere in the world today.

Prodigy, the world's first Universal Processor, is engineered to transform the capacity, efficiency and economics of datacenters through its industry-leading performance for hyperscale, high-performance computing and AI workloads. When complete, the Prodigy-powered system will deliver a 25x multiplier vs. the world's fastest conventional supercomputer built just this year, and will achieve AI capabilities 25,000x larger than models for ChatGPT4.

Tesla first revealed plans for its Dojo D1 training chip back in 2021, with hopes of it powering self-driving technology in the near future. The automative division has relied mostly on NVIDIA over the ensuing years, but is seemingly keen to move onto proprietary solutions. Media reports from two years ago suggest that 5760 NVIDIA A100 GPUs were in play to develop Tesla's advanced driver-assistance system (Autopilot ADAS). Tom's Hardware believed that a $300 Million AI supercomputer cluster—comprised of roughly 10,000 NVIDIA H100 GPUs—was powered on last month. Recent reports emerging from Taiwan suggest that Tesla is doubling Dojo D1 supercomputer chip orders with TSMC.

An Economic Daily report posits that 10,000 Dojo D1 are in a production queue for the next year, with insiders believing that Tesla is quietly expressing confidence in its custom application-specific integrated circuit (ASIC). An upcoming order count could increase for the next batch (in 2025). The article hints that TSMC's "HPC-related order momentum has increased thanks to Tesla." Both organizations have not publicly commented on these developments, but insider sources have disclosed some technical details—most notably that the finalized Dojo design: "mainly uses TSMC's 7 nm family process and combines it with InFO-level system-on-wafer (SoW) advanced packaging."

Ten miles in from Long Island's Atlantic coast, Shinjae Yoo is revving his engine. The computational scientist and machine learning group lead at the U.S. Department of Energy's Brookhaven National Laboratory is one of many researchers gearing up to run quantum computing simulations on a supercomputer for the first time, thanks to new software.

Yoo's engine, the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), is using the latest version of PennyLane, a quantum programming framework from Toronto-based Xanadu. The open-source software, which builds on the NVIDIA cuQuantum software development kit, lets simulations run on high-performance clusters of NVIDIA GPUs. The performance is key because researchers like Yoo need to process ocean-size datasets. He'll run his programs across as many as 256 NVIDIA A100 Tensor Core GPUs on Perlmutter to simulate about three dozen qubits—the powerful calculators quantum computers use. That's about twice the number of qubits most researchers can model these days.

The Exascale supercomputer arms race is making everyone invest their resources into trying to achieve the number one spot. Some countries, like China, actively participate in the race with little proof of their work, leaving the high-performance computing (HPC) community wondering about Chinese efforts on exascale systems. Today, we have some information regarding the next-generation Sunway system, which is supposed to be China's first exascale supercomputer. Replacing the Sunway TaihuLight, the next-generation Sunway will reportedly boast over 40 million cores in its system. The information comes from an upcoming presentation for Supercomputing 2023 show in Denver, happening from November 12 to November 17.

The presentation talks about 5 ExaFLOPS in the HPL-MxP benchmark with linear scalability on the 40-million-core Sunway supercomputer. The HPL-MxP benchmark is a mixed precision HPC benchmark made to test the system's capability in regular HPC workloads that require 64-bit precision and AI workloads that require 32-bit precision. Supposedly, the next-generation Sunway system can output 5 ExaFLOPS with linear scaling on its 40-million-core system. What are those cores? We are not sure. The last-generation Sunway TaihuLight used SW26010 manycore 64-bit RISC processors based on the Sunway architecture, each with 260 cores. There were 40,960 SW26010 CPUs in the system for a total of 10,649,600 cores, which means that the next-generation Sunway system is more than four times more powerful from a core-count perspective. We expect some uArch and semiconductor node improvements as well.

Cerebras Systems, the pioneer in accelerating generative AI, and G42, the UAE-based technology holding group, today announced Condor Galaxy, a network of nine interconnected supercomputers, offering a new approach to AI compute that promises to significantly reduce AI model training time. The first AI supercomputer on this network, Condor Galaxy 1 (CG-1), has 4 exaFLOPs and 54 million cores. Cerebras and G42 are planning to deploy two more such supercomputers, CG-2 and CG-3, in the U.S. in early 2024. With a planned capacity of 36 exaFLOPs in total, this unprecedented supercomputing network will revolutionize the advancement of AI globally.

"Collaborating with Cerebras to rapidly deliver the world's fastest AI training supercomputer and laying the foundation for interconnecting a constellation of these supercomputers across the world has been enormously exciting. This partnership brings together Cerebras' extraordinary compute capabilities, together with G42's multi-industry AI expertise. G42 and Cerebras' shared vision is that Condor Galaxy will be used to address society's most pressing challenges across healthcare, energy, climate action and more," said Talal Alkaissi, CEO of G42 Cloud, a subsidiary of G42.

When Lawrence Livermore National Laboratory (LLNL) announced the creation of a two-ExaFLOP supercomputer named El Capitan, we heard that AMD would power it with its Instinct MI300 accelerator. Today, LNLL published a Tweet that states, "We've begun receiving & installing components for El Capitan, @NNSANews' first #exascale #supercomputer. While we're still a ways from deploying it for national security purposes in 2024, it's exciting to see years of work becoming reality." As published images show, HPE racks filled with AMD Instinct MI300 are showing up now at LNLL's facility, and the supercomputer is expected to go operational in 2024. This could mean that November 2023 TOP500 list update wouldn't feature El Capitan, as system enablement would be very hard to achieve in four months until then.

The El Capitan supercomputer is expected to run on AMD Instinct MI300A accelerator, which features 24 Zen4 cores, CDNA3 architecture, and 128 GB of HBM3 memory. All paired together in a four-accelerator configuration goes inside each node from HPE, also getting water cooling treatment. While we don't have many further details on the memory and storage of El Capitan, we know that the system will exceed two ExFLOPS at peak and will consume close to 40 MW of power.

The AI hype continues to push hardware shipments, especially for servers with GPUs that are in very high demand. Another example is the latest feat of AI startup, Inflection AI. Building foundational AI models, the Inflection AI crew has secured an order of 22,000 NVIDIA H100 GPUs and built a supercomputer. Assuming a configuration of a single Intel Xeon CPU with eight GPUs, almost 700 four-node racks should go into the supercomputer. Scaling and connecting 22,000 GPUs is easier than it is to acquire them, as NVIDIA's H100 GPUs are selling out everywhere due to the enormous demand for AI applications both on and off premises.

Getting 22,000 H100 GPUs is the biggest challenge here, and Inflection AI managed to get them by having NVIDIA as an investor in the startup. The supercomputer is estimated to cost around one billion USD and consume 31 Mega-Watts of power. The Inflection AI startup is now valued at 1.5 billion USD at the time of writing.

Earlier this week Microsoft revealed its roadmap for the building of a proprietary quantum supercomputer. The company's research department has been making progress with the elusive building blocks of topological qubits over a number of years. Microsoft's VP of advanced quantum development - Krysta Svore - has informed TechCrunch that their team anticipates it taking under ten years to construct and complete a quantum supercomputer utilizing qubits, with a view to perform a reliable one million quantum operations per second. Svore stated: "We think about our roadmap and the time to the quantum supercomputer in terms of years rather than decades."

Majorana-based qubits are extremely difficult to create, but worth the effort due to being inherently stable. Microsoft's quantum team has dedicated itself to hitting a first milestone, with more devices developed and data collected since last year's major breakthrough. Svore reiterates: "Today, we're really at this foundational implementation level...We have noisy intermediate-scale quantum machines. They're built around physical qubits and they're not yet reliable enough to do something practical and advantageous in terms of something useful. For science or for the commercial industry. The next level we need to get to as an industry is the resilient level. We need to be able to operate not just with physical qubits but we need to take those physical qubits and put them into an error-correcting code and use them as a unit to serve as a logical qubit." Svore's team is focusing more on the building of hardware-protected qubits, that are tiny - "smaller than 10 microns on a side" with performance of one qubit operation in less than a microsecond.

What's New: The Aurora supercomputer at Argonne National Laboratory is now fully equipped with all 10,624 compute blades, boasting 63,744 Intel Data Center GPU Max Series and 21,248 Intel Xeon CPU Max Series processors. "Aurora is the first deployment of Intel's Max Series GPU, the biggest Xeon Max CPU-based system, and the largest GPU cluster in the world. We're proud to be part of this historic system and excited for the groundbreaking AI, science and engineering Aurora will enable."—Jeff McVeigh, Intel corporate vice president and general manager of the Super Compute Group

What Aurora Is: A collaboration of Intel, Hewlett Packard Enterprise (HPE) and the Department of Energy (DOE), the Aurora supercomputer is designed to unlock the potential of the three pillars of high performance computing (HPC): simulations, data analytics and artificial intelligence (AI) on an extremely large scale. The system incorporates more than 1,024 storage nodes (using DAOS, Intel's distributed asynchronous object storage), providing 220 terabytes (TB) of capacity at 31TBs of total bandwidth, and leverages the HPE Slingshot high-performance fabric. Later this year, Aurora is expected to be the world's first supercomputer to achieve a theoretical peak performance of more than 2 exaflops (an exaflop is 1018 or a billion billion operations per second) when it enters the TOP 500 list.

Today, AMD (NASDAQ: AMD) showcased its high performance computing (HPC) leadership at ISC High Performance 2023 and celebrated, along with key partners, its first year of breaking the exascale barrier. AMD EPYC processors and AMD Instinct accelerators continue to be the solutions of choice behind many of the most innovative, green and powerful supercomputers in the world, powering 121 supercomputers on the latest TOP500 list.

"AMD's mission in high-performance computing is to enable our customers to tackle the world's most important challenges," said Forrest Norrod, executive vice president and general manager, Data Center Solutions Business Group, AMD. "Our industry partners and the global HPC community continue to leverage the performance and efficiency of AMD EPYC processors and Instinct accelerators to advance their groundbreaking work and scientific discoveries."

Scientific researchers need massive computational resources that can support exploration wherever it happens. Whether they're conducting groundbreaking pharmaceutical research, exploring alternative energy sources or discovering new ways to prevent financial fraud, accessible state-of-the-art AI computing resources are key to driving innovation. This new model of computing can solve the challenges of generative AI and power the next wave of innovation. Cambridge-1, a supercomputer NVIDIA launched in the U.K. during the pandemic, has powered discoveries from some of the country's top healthcare researchers. The system is now becoming part of NVIDIA DGX Cloud to accelerate the pace of scientific innovation and discovery - across almost every industry.

As a cloud-based resource, it will broaden access to AI supercomputing for researchers in climate science, autonomous machines, worker safety and other areas, delivered with the simplicity and speed of the cloud, ideally located for the U.K. and European access. DGX Cloud is a multinode AI training service that makes it possible for any enterprise to access leading-edge supercomputing resources from a browser. The original Cambridge-1 infrastructure included 80 NVIDIA DGX systems; now it will join with DGX Cloud, to allow customers access to world-class infrastructure.

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.

The Korea Intellectual Property Rights Information Service (KIPRIS) has been processing a bunch of trademark applications in recent weeks, submitted by Samsung Electronics Corporation. News outlets pointed out, earlier on this month, that the South Korean multinational manufacturing conglomerate was attempting to secure the term "Snowbolt" as a moniker for an unreleased HBM3P DRAM-based product. Industry insiders and Samsung representatives have indicated that high bandwidth memory (5 TB/s bandwidth speeds per stack) will be featured in upcoming cloud servers, high-performance and AI computing - slated for release later on in 2023.

A Samsung-focused news outlet, SamMobile, has reported (on May 15) of further trademark applications for next generation DRAM (Dynamic Random Access Memory) products. Samsung has filed for two additional monikers - "Shinebolt" and "Flamebolt" - details published online show that these products share the same "designated goods" descriptors with the preceding "Snowbolt" registration: "DRAM modules with high bandwidth for use in high-performance computing equipment, artificial intelligence, and supercomputing equipment" and "DRAM with high bandwidth for use in graphic cards." Kye Hyun Kyung, CEO of Samsung Semiconductor, has been talking up his company's ambitions of competing with rival TSMC in providing cutting edge component technology, especially in the field of AI computing. It is too early to determine whether these "-bolt" DRAM products will be part of that competitive move, but it is good to know that speedier memory is on the way - future generation GPUs are set to benefit.

With more countries creating initiatives to develop homegrown processors capable of powering powerful supercomputing facilities, India has just presented its development milestone with Aum HPC. Thanks to information from the report by The Next Platform, we learn that India has developed a processor for powering its exascale high-performance computing (HPC) system. Called Aum HPC, the CPU was developed by the National Supercomputing Mission of the Indian government, which funded the Indian Institute of Science, the Department of Science and Technology, the Ministry of Electronics and Information Technology, and C-DAC to design and manufacture the Aum HPC processors and create strong, strong technology independence.

The Aum HPC is based on Armv8.4 CPU ISA and represents a chiplet processor. Each compute chiplet features 48 Arm Zeus Cores based on Neoverse V1 IP, so with two chiplets, the processor has 96 cores in total. Each core gets 1 MB of level two cache and 1 MB of system cache, for 96 MB L2 cache and 96 MB system cache in total. For memory, the processor uses 16-channel 32-bit DDR5-5200 with a bandwidth of 332.8 GB/s. To expand on that, HBM memory is present, and there is 64 GB of HBM3 with four controllers capable of achieving a bandwidth of 2.87 TB/s. As far as connectivity, the Aum HPC processor has 64 PCIe Gen 5 Lanes with CXL enabled. It is manufactured on a 5 nm node from TSMC. With a 3.0 GHz typical and 3.5+ GHz turbo frequency, the Aum HPC processor is rated for a TDP of 300 Watts. It is capable of producing 4.6+ TeraFLOPS per socket. Below are illustrations and tables comparing Aum HPC to Fujitsy A64FX, another Arm HPC-focused design.

Implementing state-of-the-art artificial intelligence (AI) and machine learning (ML) models requires large amounts of computation, both to train the underlying models, and to serve those models once they're trained. Given the demands of these workloads, a one-size-fits-all approach is not enough - you need infrastructure that's purpose-built for AI.

Together with our partners, we offer a wide range of compute options for ML use cases such as large language models (LLMs), generative AI, and diffusion models. Recently, we announced G2 VMs, becoming the first cloud to offer the new NVIDIA L4 Tensor Core GPUs for serving generative AI workloads. Today, we're expanding that portfolio with the private preview launch of the next-generation A3 GPU supercomputer. Google Cloud now offers a complete range of GPU options for training and inference of ML models.

Tachyum today published a new white paper presenting HPC and AI supercomputer data center designs using the Prodigy Universal Processor Family, Prodigy and Prodigy 2. Tachyum Prodigy 2 was selected by Important Project of Common European Interests (IPCEI) program for Slovakia to deliver exa-scale HPC and zetta-scale AI for Europe. European Commission has accepted the funding gap of 26.4 million EUR for Tachyum, which is currently in the notification process.

Developed by Tachyum's systems, solutions, and software engineering teams, these reference designs transform data centers into universal computing centers in which HPC and AI workloads can run on the same architecture. Tachyum has developed thorough data center designs incorporating state-of-the-art solutions for computing, networking, storage, software, and cooling to address the next generation of HPC/AI applications.

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.