At the RTX "Blackwell" Editor's Day during CES 2025, NVIDIA pulled back the curtain on one of its most powerful tools: a dedicated supercomputer that has been continuously improving DLSS (Deep Learning Super Sampling) for the past six years. Brian Catanzaro, NVIDIA's VP of applied deep learning research, disclosed that thousands of the company's latest GPUs have been working round-the-clock, analyzing and perfecting the technology that has revolutionized gaming graphics. "We have a big supercomputer at NVIDIA that is running 24/7, 365 days a year improving DLSS," Catanzaro explained during his presentation on DLSS 4. The supercomputer's primary task involves analyzing failures in DLSS performance, such as ghosting, flickering, or blurriness across hundreds of games. When issues are identified, the system augments its training data sets with new examples of optimal graphics and challenging scenarios that DLSS needs to address.

DLSS 4 is the first move from convolutional neural networks to a transformer model that runs locally on client PCs. The continuous learning process has been crucial in refining the technology, with the dedicated supercomputer serving as the backbone of this evolution. The scale of resources allocated to DLSS development is massive, as the entire pipeline for a self-improving DLSS model must consist of not only thousands but tens of thousands of GPUs. Of course, a company making 100,000 GPU data centers (xAI's Colossus) must save some for itself and is proactively using it to improve its software stack. NVIDIA's CEO Jensen Huang famously said that DLSS can predict the future. Of course, these statements are to be tested when the Blackwell series launches. However, the approach of using massive data centers to improve DLSS is quite interesting, and with each new GPU generation NVIDIA release, the process is getting significantly sped up.

Today, AMD showcased its ongoing high performance computing (HPC) leadership at Supercomputing 2024 by powering the world's fastest supercomputer for the sixth straight Top 500 list.

The El Capitan supercomputer, housed at Lawrence Livermore National Laboratory (LLNL), powered by AMD Instinct MI300A APUs and built by Hewlett Packard Enterprise (HPE), is now the fastest supercomputer in the world with a High-Performance Linpack (HPL) score of 1.742 exaflops based on the latest Top 500 list. Both El Capitan and the Frontier system at Oak Ridge National Lab claimed numbers 18 and 22, respectively, on the Green 500 list, showcasing the impressive capabilities of the AMD EPYC processors and AMD Instinct GPUs to drive leadership performance and energy efficiency for HPC workloads.

NEC Corporation (NEC; TSE: 6701) has received an order for a next-generation supercomputer system from Japan's National Institutes for Quantum Science and Technology (QST), under the National Research and Development Agency, and the National Institute for Fusion Science (NIFS), part of the National Institutes of Natural Sciences under the Inter-University Research Institute Corporation. The new supercomputer system is scheduled to be operational from July 2025. The next-generation supercomputer system will feature multi-architecture with the latest CPUs and GPUs and will consist of large storage capacity and a high-speed network. This system is expected to be used for various research and development in the field of fusion science research.

Specifically, the system will be used for precise prediction of experiments and creation of operation scenarios in the ITER project, which is being promoted as an international project, and the Satellite Tokamak (JT-60SA) project, which is being promoted as a Broader Approach activity, and for design of DEMO reactors. The DEMO project promotes large-scale numerical calculations for DEMO design and R&D to accelerate the realization of a DEMO reactor that contributes to carbon neutrality. In addition, NIFS will conduct numerical simulation research using the supercomputer for multi-scale and multi-physics systems, including fusion plasmas, to broadly accelerate research on the science and applications of fusion plasmas, and as an Inter-University Research Institute, will provide universities and research institutes nationwide with opportunities for collaborative research using the state-of-the-art supercomputer.

NVIDIA today announced that xAI's Colossus supercomputer cluster comprising 100,000 NVIDIA Hopper GPUs in Memphis, Tennessee, achieved this massive scale by using the NVIDIA Spectrum-X Ethernet networking platform, which is designed to deliver superior performance to multi-tenant, hyperscale AI factories using standards-based Ethernet, for its Remote Direct Memory Access (RDMA) network.

Colossus, the world's largest AI supercomputer, is being used to train xAI's Grok family of large language models, with chatbots offered as a feature for X Premium subscribers. xAI is in the process of doubling the size of Colossus to a combined total of 200,000 NVIDIA Hopper GPUs.

NVIDIA and Foxconn are building Taiwan's largest supercomputer, marking a milestone in the island's AI advancement. The project, Hon Hai Kaohsiung Super Computing Center, revealed Tuesday at Hon Hai Tech Day, will be built around NVIDIA's groundbreaking Blackwell architecture and feature the GB200 NVL72 platform, which includes a total of 64 racks and 4,608 Tensor Core GPUs. With an expected performance of over 90 exaflops of AI performance, the machine would easily be considered the fastest in Taiwan.

Foxconn plans to use the supercomputer, once operational, to power breakthroughs in cancer research, large language model development and smart city innovations, positioning Taiwan as a global leader in AI-driven industries. Foxconn's "three-platform strategy" focuses on smart manufacturing, smart cities and electric vehicles. The new supercomputer will play a pivotal role in supporting Foxconn's ongoing efforts in digital twins, robotic automation and smart urban infrastructure, bringing AI-assisted services to urban areas like Kaohsiung.

The zettascale era is officially on the map, as Japan has announced plans to develop a successor to its renowned Fugaku supercomputer. The Ministry of Education, Culture, Sports, Science and Technology (MEXT) has set its sights on creating a machine capable of unprecedented processing power, aiming for 50 ExaFLOPS of peak AI performance with zettascale capabilities. The ambitious "Fugaku Next" project, slated to begin development next year, will be headed by RIKEN, one of Japan's leading research institutions, in collaboration with tech giant Fujitsu. With a target completion date of 2030, the new supercomputer aims to surpass current technological boundaries, potentially becoming the world's fastest once again. MEXT's vision for the "Fugaku Next" includes groundbreaking specifications for each computational node.

The ministry anticipates peak performance of several hundred FP64 TFLOPS for double-precision computations, around 50 FP16 PFLOPS for AI-oriented half-precision calculations, and approximately 100 PFLOPS for AI-oriented 8-bit precision calculations. These figures represent a major leap from Fugaku's current capabilities. The project's initial funding is set at ¥4.2 billion ($29.06 million) for the first year, with total government investment expected to exceed ¥110 billion ($761 million). While the specific architecture remains undecided, MEXT suggests the use of CPUs with special-purpose accelerators or a CPU-GPU combination. The semiconductor node of choice will likely be a 1 nm node or even more advanced nodes available at the time, with advanced packaging also used. The supercomputer will also feature an advanced storage system to handle traditional HPC and AI workloads efficiently. We already have an insight into Monaka, Fujitsu's upcoming CPU design with 150 Armv9 cores. However, Fugaku Next will be powered by the Monaka Next design, which will likely be much more capable.

The rollout of SiPearl's much-anticipated Rhea processor for European supercomputers has been pushed back by a year to 2025, but the delay comes with a silver lining - a significant upgrade in core count and potential performance. Originally slated to arrive in 2024 with 72 cores, the homegrown high-performance chip will now pack 80 cores when it eventually launches. This decisive move by SiPearl and its partners is a strategic choice to ensure the utmost quality and capabilities for the flagship European processor. The additional 12 months will allow the engineering teams to further refine the chip's architecture, carry out extensive testing, and optimize software stacks to take full advantage of Rhea's computing power. Now called the Rhea1, the chip is a crucial component of the European Processor Initiative's mission to develop domestic high-performance computing technologies and reduce reliance on foreign processors. Supercomputer-scale simulations spanning climate science, drug discovery, energy research and more all require astonishing amounts of raw compute grunt.

By scaling up to 80 cores based on the latest Arm Neoverse V1, Rhea1 aims to go toe-to-toe with the world's most powerful processors optimized for supercomputing workloads. The SiPearl wants to utilize TSCM's N6 manufacturing process. The CPU will have 256-bit DDR5 memory connections, 104 PCIe 5.0 lanes, and four stacks of HBM2E memory. The roadmap shift also provides more time for the expansive European supercomputing ecosystem to prepare robust software stacks tailored for the upgraded Rhea silicon. Ensuring a smooth deployment with existing models and enabling future breakthroughs are top priorities. While the delay is a setback for SiPearl's launch schedule, the substantial upgrade could pay significant dividends for Europe's ambitions to join the elite ranks of worldwide supercomputer power. All eyes will be on Rhea's delivery in 2025, mainly from Europe's governments, which are funding the project.

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.

Today, in advance of ISC High Performance 2024, SpiNNcloud Systems announced the commercial availability of its SpiNNaker2 platform, a supercomputer-level hybrid AI high-performance computer system based on principles of the human brain. Pioneered by Steve Furber, designer of the original ARM and SpiNNaker1 architectures, the SpiNNaker2 supercomputing platform uses a large number of low-power processors for efficiently computing AI and other workloads.

First-generation SpiNNaker1 architecture is currently used in dozens of research groups across 23 countries worldwide. Sandia National Laboratories, Technical University of München and Universität Göttingen are among the first customers placing orders for SpiNNaker2, which was developed around commercialized IP invented in the Human Brain Project, a billion-euro research project funded by the European Union to design intelligent, efficient artificial systems.

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.