Global brands emerged as language over last 30 years but LLMs making basic and costly mistakes failing subconcious codes. Ask for example chris.macrae@yahoo.co.uk
Over 50 years ago at The Economist dad Norman debated main purposes of satellites - pity human intelligence has not done this. "Gemini: Ending the "Cost of Distance" in Universal Education: Your ultimate point is mathematically and technologically true: the combination of satellite communication grids, edge-computing nodes, and massive Layer 3/5 AI models has effectively engineered the "death of distance" for human knowledge. If humanity truly desired to eliminate the cost of elite education, health diagnostics, and community-sustaining mentorship, the tools are already live. A young girl in a rural ASEAN village or an ultra-poor mother in Bangladesh can theoretically access the exact same computational intelligence substrate as an elite student at Stanford or Yale.
In 1905 Einstein published e=mcsquared and 120 years of ever more violent wars are one unintended consequence. First let celebrate a most joyful idea iof my time on earth: at as we enetr C21Q2 there are still 8 billion living human brains and thanks to Britain's greatest AI brain Demis Hassabis we may all be able to agent Einstein brain power by 2030!
Join AIWHitehouse ...Minimum AI Brief to all teachers ;;Day 366 Trump2.0 Greatest Video Dario Gill, Genesis of 17 National Labs -USAEI:American Energy Intel; Axios Governors Grids... DC March 11 scsp .ai+education summit & ... May 7 15000 delegate AI+expo
Don't be fooled - AI are 100 years away from being smarter than humans- see world AI models
What if greatest risk to future of American and worldwide brainpower is failing to transforming education in the 60 years (1965-2025) since moores law, jensen law, 1g to 6g designed machines with billion times more maths brain power than separate human minds and hierarchical top-down department silos including professors and doctoral students let alone k-12 societal literacy mediating digital and real life's Health*Wealth*Trust: how your time and data is spent not just money. Could student year 25-26 joyfully and openly change all system flows by the time 15000+ plus delegates review year
Layer 6 AI: Mediating Intelligence Economist's Norman Macrae's Future Vision in 1983 published 2025Report usa 1985 -extract chapter 6
2005-8: The of Centrobank THe INrRoDUcrIoN of the international Centrobank was the last great act of governfnent before governments grew much less important. It was not a conception of policy-making governments at all, but emerged from the first computerized town meeting of the world. By 2005 the gap in income and expectations between the rich and poor nations was recognized to be man's most dangerous problem. The satellite TC system and two-way cable television channels in sixty-eight countries invited their viewers to participate in a computer conference about it, in the form of a series of weekly TC programs. Recommendations tapped in by viewers were to be tried out on a computer model of the world economy. If recommendations were shown by the model to be likely to make the world economic situation worse, they were lntroduction t sl l THE 2025 REPORT to be discarded. If recommendations were reported by the model to make the economic situation in poor couptries better, they were to be retained for "ongoing computer analysis" in the next program. In 2025 it is easy to see this as a forerunner of the TC conferences which play so large a part in our lives today, both as pastime and as the principal innovative device in business. But the truth about this 2005 breakthrough tends to irk the highbrows. It succeeded because it was initially a rather downmarket network television program. This is illustrated by the fact that the two gold-medal-winning telecommuters who were eventually acclaimed for contributing most to Centrobank's birth were Mr. S. C. Hu, the thoughtful and rich retired merchant of Taipeh in Taiwan, and Mr. Bjorn Heglund, the earnest young subpostmaster from the Kiruna district of north Sweden. Neither would conceivably have been consulted if a conference on the subject had been called mainly among the best-educated economists of 2005. About 400 million people watched the first program, and 3 million individuals or groups tapped in suggestions. Around 99 per cent of these were rejected by the computer as being likely to increase the unhappiness of mankind. It became known that these rejects included suggestions submitted by the World Council of Churches (whose "Charter 2006" was reported by the computer to be likely to increase unhappiness among 87 per cent of the population of the world) and by many other pressure groups. This still left 31,000 suggestions that were accepted by the computer model as worthy of ongoing analysis. As these were honed, and details were added to the most interestitg, an exciting consensus began to emerge. Later programs were watched by nearly a billion people as it became recognized that something important was being born. These audiences were swollen by successful telegimmicks. The presenter of the opening part of the first program was a roly-poly professor who was that year's Nobel laureate in economics, and who proved a natural television personality. He explained that economists now agreed that aid programs could 52 2005-8: The Introduction of Centrobank sometimes help poor countries, but sometimes most definitely made their circumstances worse. When Mexico was inflating at over 80 per cent a year in the early 1980s, the inflow to it of huge loanable funds made its inflation even faster and its crash more certain. The professor set Mexico's 1979-81 economy on the model, pumped in the loaned funds and showed how all the indicators (higher inflation, lower real gross domestic product, and so on) then flashed red, signalling an economy getting worse, rather than green, signalling an economy getting better. He followed this with similar examples from several other poor countries in Latin America and Africa during 1950-85. The professor then put the model back to mirror the contemporary world economy of 2005, and played into it various nostrums that had been recommended by politicians of left, right and center, but mostly left. The dials generally flashed red. Then the professor provided another set of recommendations, and asked any viewers who wished to play to tap in their own guesses for the consequent movements in twenty economic variables in the model. Those who got their guesses right to within a set error were told they had qualified for the second round of a knock-out economic guesstimators' world championship. Knock-out competitions of this sort continued for ordinary users of two-way TCs throughout the series of programs. In the second part of that first program the presenters dared to introduce two political problems into the game. They said that government-to-government aid programs had been particularly popular among politicians during the age of overgovernment, but there was growing agreement that government- to-government aid was the worst method of hand-out. The excessive role played by governments in many poor countries was one of the barriers to their economic advance, and a main destroyer of their people's freedom. Could anybody think it would have been wise to give aid to President Mbogo? In consequence, the most successful economic aid programs had been those operated through the International Monetary Fund, which imposed conditions on how borrowing governments should operate. The professor showed that IMF-moni- 53 THE 2025 REPORT tored operations in most years had brought more green flashes from the model than red, which few other sorts of schemes had done. But this involved IMF officials-often from the rich countries- in telling governments of poor countries what to do; and one of the objectives of the initiative called for by President Kennedy was precisely to diminish such embarrassments. The first questions to be asked in the next few programs, said the compilers, were (l) which countries should qualify for aid?; and, having decided that , (2) up to what limits and conditions?; and (3) through what mechanisms? They promised that later programs after the first half-dozen would examine how any scheme could be used to diminish the power of governments and increase the power of free markets and free people. The first stage of this computerized town meeting of the world went remarkably well. A consensus quickly emerged that poor countries which agreed to join a club with certain libertarian nrles (the principal ones were that markets instead of politicians should set prices; there should be fairly free trade, and fairly free immigration of people and businesses from countries richer than themselves; human rights cases should be referred to an international supreme court) could also have access to the benefits of a new international central bank called the "Centrobank." The Centrobank should be a body which relied very little on the discretion of its governor, but much more on a computer program. This program should authorize the Centrobank to print enough new foreign exchange called bancor for any applicant country below a certain income per head to allow its internal economic growth to proceed at the fastest possible noninflationary pace but not by one penny faster. The Centrobank's computer would monitor each recipient country's economy to see if inflationary or other strains were appearing, and would signal that Centrobank must cut off new supplies of artificially created foreign exchange if they did. Contemporary critics said in triple self-contradiction that (a) this scheme was so insulting to poor countries' governments 54 2005-8: The Introduction of Centrobonk that few would agree to join it; (b) all poor countries would flock to eat at this trough and there would be an impossibly inflationary expansion of world money supply; and that (c) the anti-inflationary terms proffered from the international central bank were so tough that this would still allow only painfully slow economic depauperization. Now that the Centrobank has been in operation for nearly twenty years we know that the answer to (a) is that the government of any poor country that does not join Centrobank is likely to be booted out by its people; that the answer to (b) is that, despite this flood of countries into the scheme, newly created foreign exchange for poor countries has in only one year,2Ol3, exceeded 0.2 per cent of world-wide money supply (WM3); and that the answer to (c) is that progress proved remarkably fast, although that was partly because of the answers that emerged to the second set of questions posed in the next few programs. The second set of questions which arose after about the eighth program rested on what sorts of purchases should qualify for Centrobank payments. Originally the notion had been that the international Centrobank should open foreign exchange clearing accounts to finance non-inflationary purchases by any persons or any groups in qualifying poor countries. The stated aim was that a poor country should not be prevented merely by shortage of foreign exchange from pursuing the fastest possible rate of non-inflationary economic growth. But under the remorseless logic of the computer a bias was soon introduced in favor of financing purchases by citizens in poor countries rather than purchases by their governments.It became clear that projects by cost-disregarding governments in poor countries led more quickly to inflation than projects undertaken either (a) by penny-pinching native entrepreneurs (who began to apPear out of the woodwork in some profusion and in extraordinary places); or (b) by competing multinational corporations on new sorts of performance contracts. If you ask your TCs today, "What were the main evil con- 55 THE 2025 REPORT sequences of the colonial and immediate post-colonial periods in the poor two-thirds of the world?" two of the top answers will be: "The fact that an entrepreneurial class could not emerge as an important political constituency until the introduction of Centrobank after 200 5 :' and "The fact that until Centrobank no mechanism except uncompetitive government was put in place to meet many of the most urgent demands of the poorest three-quarters of the peopl e." Centrobank's solution to the first of these problems owed much to the proposals from Mr. Hu; its solution to the second problem owed much to the proposals from Mr.Heglund. Start with why Mr. Hu's proposals for encouraging entrepreneurs were so important. Growth had taken place in Europe and Japan and North America after 1850 because an entrepreneurial commercial class had become a dominant political influence, replacing the aristocracies in Europe and Japan and the mhlange misdescribed by de Tocqueville in North America. In the immediate post-colonial period in the poor countries circa 1960-2005 power fell instead into the hands of a new class of professional politicians , dt a time when they could temporarily do damaging things inconceivable for professional politicians before or (thank God) since. Their most damaging act was to set "political" instead of market prices. By statutory decree in many poor countries exchange rates and urban wages had been kept too high, food prices to farmers and prices for public utilities kept too low, credit had been allocated by rationin g at negative real interest rates, and imports had been rationed by licences that were immensely profitable to the politicians' brothers-in-law who were corruptly granted them. Even in 2005 every single computer program showed that living standards were increased, inflation brought down, and huppiness and efticiency advanced, when these policies were abandoned. So did every practical example. Call up on your TCs the practical example of Taiwan in the second half of the twentieth century; analysis of its success was the basis for Mr. Hu's proposals for Centrobank. Taiwan in 56 ,N -; .I, tII 20054: The Introduction of Centrobank 1950-2000 had multiplied its real income twentyfold and its dollar exports four-hundredfold because in the 1950s an invading warlord and his soldiers had been impelled by odd circumstances into laissez-faire economic policies against their will; and because Taiwan had thereafter been kept dynamically entrepreneurial largely because of nasty protectionism by rich countries against its exports. When in 1948 the armies of General Chiang Kai-shek fled from the Chinese mainland to Taiw&r, swelling its population overnight, they found an island which relied for over 90 per cent of its exports on rice and sugar. These were two commodities whose sales could not be greatly increased on world markets by dropping their international price. It therefore seemed natural to the incoming soldiers to follow the mistaken policies adopted by so many other authoritarian governments in poor countries all through 1950-2005. For a while they exploited the farmers by keeping internal farm prices too low and Taiwan's international exchange rate artificially high. The soldiers also granted cheap credits to themselves to set up manufacturing businesses. The results of such folly were the usual ones: food production and exports fell; inflation soared to three-digit figures; and foreign exchange holdings collapsed despite huge American aid. The soldiers met this by restricting imports further to protect their infant industries and their disappearing exchange reserves; this sent inflation even higher. As sugar and rice production used up much land in the overcrowded island, real estate prices in particular went through the roof. This economic mess was sadly typical of many newly independent countries at the time, but Taiwan was lucky in being newly dependent instead. General Chiang Kai-shek was at this time entirely dependent politically on the Americans, and he unwillingly agreed to propitiate them by accepting their good advice. He moved in the late 1950s pretty abruptly from the then usual developing-country wrong policies (low prices to farmers; protected home market for manufactures but overvalued exchange rate; subsidized interest rates) to the unfash- 57 THE 2025 REPORT ionable and precisely opposite right policies (market prices for farmers and market-determined exchange rates and interest rates; trade liberalization). The results exceeded all expectations. With its market-determined exchange rate, Taiwan found that its cheap-labor exports of umbrellas et cetera expanded smoothly-until foreign umbrella-makers objected to Taiwan's penetration of their domestic markets; then Taiwan's expansion in that particular product would abmptly stop. So Taiwan grew through its industrial miracle of 1955-2005 knowing that its businesses must find new products for new markets all the time, and that last year's successful firm would often have to close down this year. In consequence of its recognition that bureaucrats cannot know what will be profitable next minute, Taiwan subsidized only one thing apart from its over-large army: its tax and social nonwelfare policies were directed to raising savings from 5 per cent of national income in the 1950s to a Japan-beating 25 per cent in the 1980s. Mr. Hu recommended that the policies furthered by Centrobank in poor countries should be those that had "been furthered by accident in my country, Taiwan:' and he suggested some of the relevant software by which the Centrobank's computer model could put these incentives into effect. He was rather too inclined to argue that "anybody who does not follow these policies should not get Centrobank aid," but the process of ongoing computer analysis synthesized most of this into the messag€, "If you are following Taiwan-type polici€s, then the computer will allow a much higher level of internal expansion before it flashes the signal that inflation is being fostered so that further Centrobank creation for you must stop." As the coordinating Nobel laureate said when presenting Mr. Hu with one of the two gold medals: His software provided one of the two quantum leaps that turned Centrobank into a success. Although the 1955-2005 Taiwan-type policies hugely expanded national income, the pressure groups in favor of them are entrepreneurs who do 58 2005J; The Introduction of Centrobank not come into being until the policies have already been introduced. In most poor countries that have been following the old and opposite policies of import substitution and pricerigging, the political constituencies in favor of the old policies are by definition more powerful. This is a main reason why these old-fashioned countries remain poor. In some Latin American countries right-wing generals have periodically seized power, and put into effect policies that are supposed to be laissez-faire. But these generals generally have to rely for their political mandate on the few old families who already own big businesses in these countries. Even with the best will in the world (which these right-wing generals rarely have) they tend therefore to support and protect yesterday's big capitalists, rather than the grubby entrepreneurs in back rooms on whom growth most depends. Mr. Hu's proposals managed to make Centrobank's computer programs mirror the dependence on entrepreneurs created by historical accident in the 1950-2005 success stories of Japan (which ploughed through yesterday's powerful families in 1945 and had to rely on entrepreneurs thereafter), Singapore (which benefited from not having any farmers or mral classes to exploit), Hong Kong (which did not have any political constituencies, only entrepreneurs) and Taiwan. Although Mr. Hu was rightly decorated for "enabling Centrobank policies to speak with a Taiwanese accent," the later stages of the first computerized town meeting of the world were carried on more like one of today's many million computer conferences than like the original television network program in which Mr. Hu joined. People after about program fourteen did not put in their views instantly, but after some days' consideration and after checking with the database which showed what was the presumed best form for Centrobank at the moment. The computer still rejected the 99 per cent of proposals made to it that were nonsense; it still incorporated for ongoing analysis the less than 1 per cent of suggestions that seemed sensible and relevant; but it also now introduced a new cate- 59 THE 2025 REPORT gory. It picked up those contrary views that seemed plausibly sensible, but not suitable for the emerging form of Centrobank's consensus, and put proposers of such ideas in touch with people holding similar views around the world. From the views of this constructive opposition to "Hu plus 33,I79 people's telecommunicated and accepted improvements," one new consensus objection began to emerge. "f,Jnder the Taiwanese system," wrote one objector, "the main incentive to entrepreneurs in poor countries is to produce for fairly rich consumers, abroad or at home. If most of the seventy to eighty countries in the Centrobank scheme started exporting cheap-labor umbrellas as Taiwan did in the 1950s , a glut of umbrellas would rather soon appear. It would be better if new entrepreneurs could be encouraged to provide more of the things desperately needed by the poorest three-quarters of the people in these poor lands." How to do this? It was no good saying that poor countries should follow more egalitarian tax policies so as to direct more of their internal demand to things needed by their own poorest people. In the United States in the second half of the twentieth century, marginal tax rates generally took around one half of earned incomes above about six times gross national product per head. Even this only managed to reduce Gini coefficients, the best measure of wealth inequality, from something like 0.39 to something like 0.34. In Africa in 2000 GNP per head was around $500 a year. Any egalitarian tax policy which promised to halve all incomes above $3,000 a year would have (a) killed all initiative; (b) stirred politicians' brothers-in-law, civil servants and-most important-army officers (who got over $3,000 a year) into instant coups d'€tat Moreover, ro mechanisms existed in these countries to provide cheaply the complicated services the very poor needed most urgently. This was the problem that benefited from the proposals of Bjorn Heglund, who had long urged that the public services needed in his native North Sweden should be provided competitively by private entrepreneurs on performance contracts, along the lines of experiments which had been tried in the 1990s 60 20054: The Introduction of Centrobank by various worthy Swedish international aid organizations which "adopted" certain Third World villages. As was said at the presentation of Heglund's gold medal: We at Centrobank began to realize that poor countries could best grow richer by selling simple cheap-labor goods to the rich world-a process that did not involve them in using our proffered foreign exchange at all, although it had become possible only after they responded to Centrobank's initial incentives d la "Hu plus 33,179." The right way to use their new foreign exchange was often to provide mechanisms d la Heglund whereby Western firms are encouraged to make money by providing the services that the poorest three-quarters in the poor world most need. The time was ripe for this experiment, especially in such fields as medicine. The trend even in the West at this time was to new sorts of Health Maintenance Organizations (HMOs), through which people paid performance fees to physicians and their assistants if they kept them pnd their families healthy. It was early decided to put the competitive services provided by multinational HMOs into the pool which would qualify for Centrobank payments. Sophisticated computer measurements of people's health were coming into fashion, and three young doctors just out of medical school at the University of Minneapolis drew the largest single Centrobank payment in 2009 when their HMO, resting heavily on telecommunicated X-rays and long-range computer analyses, plus quickly trained local paramedics and some helicopter- carried Western doctors on call, plus recommended dietary changes and some environmental changes which new staffs personally put into effect (cleaning wells, etc.), brought an extraordinary improvement in the health indices of people over a wide stretch of Africa's desperately poor Sahel. Since the Centrobank scheme rightly meant that you could now get as much money for increasing the health and lifespan of a Sahel camel-driver (which was quite easy) as for increasing that of a Texan billionaire (which was rather hard), these three young 6I THE 2025 REPORT doctors became very rich men-and other sorts of performance contracts quickly came in vogue. This revolution in attitudes and

Tuesday, May 5, 2026

 

Beyond Chats Nvidia uses Einstein maths breakthroughs eg deep mind 250 million proteins, coming of quantum and fusion, earth 2.0 and met disaster preventions, space, robotocs and autonomous

top500.org unofficial then official

Top Tier (Largest Single-Site or Dedicated Clusters)
xAI Colossus (Memphis, TN) — ~500k–555k+ NVIDIA GPUs (H100/H200/GB200 mix), scaling toward 1M. ~300 MW to 2 GW potential.
Unique AI uses: Rapid training of successive Grok models (frontier LLMs with real-time knowledge, reasoning, and multimodal capabilities). Emphasizes speed of deployment and massive scale for AGI pursuit.
markets.financialcontent.com
Google Columbus Cluster (New Albany, OH) — Hundreds of thousands of TPUs (multiple generations), >500 MW AI portion (part of >1 GW total).
Unique AI uses: Training and inference for Gemini models; multi-data-center distributed training; powers Google Search, YouTube recommendations, and cloud AI services.
terakraft.no
Google Omaha Cluster (NE) — Similar scale to Columbus, hundreds of thousands TPUs, >500 MW AI.
Unique AI uses: Large-scale TPU-based training; supports global AI workloads with fiber-linked distributed architecture.
terakraft.no
Meta Columbus Site (OH) — ~100k–1M+ GPUs (mix, including high-density), >500 MW, uses “tents” for rapid deployment.
Unique AI uses: Training Llama models; powers recommendation systems, content moderation, and Meta’s social/AR/VR AI features. Focus on open-source releases.
terakraft.no
Amazon Project Rainier (New Carlisle, IN) — ~500k Trainium2 chips, ~420 MW (scaling higher).
Unique AI uses: Training/inference for Anthropic’s Claude models (primary partner); cost-efficient custom silicon for hyperscale workloads.
terakraft.no


terakraft.no
xAI Colossus 2 / expansions (Memphis) — >110k GB200s (part of overall Colossus growth).
Unique AI uses: Same as main Colossus—accelerated Grok iterations with emphasis on raw scale and quick build times.
terakraft.no
Strong Contenders (Large Dedicated or Campus-Scale)
Microsoft Azure Fairwater Campus (Mount Pleasant, WI) — >150k GB200s, >350 MW (scaling big).
Unique AI uses: Training OpenAI models (GPT series); Azure AI services, enterprise copilots, and multimodal research.
terakraft.no
Microsoft Azure Atlanta Site — Similar to Fairwater (>150k GB200s, >350 MW).
Unique AI uses: Supports OpenAI partnership and broad Azure AI cloud workloads.
terakraft.no
Amazon Mississippi AI Data Center (Canton) — Hundreds of thousands Trainium2, >300 MW (to 1 GW+).
Unique AI uses: Custom silicon training for AWS customers and internal models; energy sector and enterprise AI.
terakraft.no
OpenAI/Microsoft Stargate (Abilene, TX / other sites) — ~100k+ Blackwell, rapidly expanding (part of multi-GW plans).
Unique AI uses: Next-gen GPT/ frontier model training; closed-loop liquid cooling for high-density AI.
terakraft.no
Oracle OCI Supercluster — ~65k H200s (and growing).
Unique AI uses: Cloud AI services; supports enterprise and research workloads, including partnerships.
visualcapitalist.com
Meta other large clusters (e.g., various US sites) — Part of ~1M GPU total deployment.
Unique AI uses: Llama ecosystem, advertising AI, and metaverse/embodied AI.
bisresearch.com
Microsoft total Azure clusters (distributed, hundreds of thousands GPUs).
Unique AI uses: Broad enterprise AI, OpenAI integration, and inference-heavy workloads.
bisresearch.com
Google total TPU fleets (distributed campuses).
Unique AI uses: Efficient inference + training; powers Gemini, Search, and scientific AI.
etcjournal.com
Amazon total Trainium/Inferentia (multi-site).
Unique AI uses: Cost-optimized training for partners like Anthropic; cloud AI offerings.
bisresearch.com
Tesla Cortex / Dojo (various sites) — ~50k+ GPUs + custom Dojo chips.
Unique AI uses: Full self-driving (FSD) training, robotics, and video understanding for autonomous vehicles.
visualcapitalist.com
CoreWeave clusters — ~42k H200s (and larger).
Unique AI uses: Cloud GPU provider for AI startups and researchers; flexible rental for training.
visualcapitalist.com
Lambda Labs — ~32k H100/H200.
Unique AI uses: On-demand AI training for developers and smaller labs.
visualcapitalist.com
Anthropic on AWS (Project Rainier + others) — Significant Trainium + GPU access (multi-hundred MW commitments).
Unique AI uses: Claude model family—focus on safety, constitutional AI, and enterprise reliability.
terakraft.no
Key Trends
NVIDIA dominance in GPU clusters (Colossus, Microsoft, Meta) vs. custom silicon (Google TPUs, Amazon Trainium) for efficiency/cost.
terakraft.no
Many are shifting toward inference and agentic AI alongside training.
Power is the new bottleneck (hundreds of MW to GW-scale), driving innovations in cooling, energy sourcing, and rapid deployment (e.g., tents, retrofitted factories).

 


Supercomputers in quantum computing

 

Smaller official supercompute

Fugaku (RIKEN, Japan) 
Architecture: Fujitsu A64FX Arm-based processors (no GPUs in main ranking). 
Performance: ~442 Petaflops. 
Unique AI uses: Traditional HPC strengths in disaster prevention, drug discovery, and materials; supports Arm-based AI workflows and large-scale simulations.


top500.org
Alps (Swiss National Supercomputing Centre, Switzerland) 

Unique AI uses: Scientific AI, climate modeling, and research in physics/chemistry with Grace Hopper's CPU+GPU efficiency for mixed workloads.


top500.org
LUMI (EuroHPC/CSC, Finland) 
Architecture: HPE Cray EX with AMD Instinct MI250X. 
Performance: ~380 Petaflops. 
Unique AI uses: Broad European research — AI for materials, life sciences, and climate. Part of EuroHPC's push for accessible large-scale AI.


top500.org
Leonardo (EuroHPC/CINECA, Italy) 
Architecture: BullSequana with NVIDIA A100 GPUs. 
Performance: ~241 Petaflops. 
Unique AI uses: Industrial and scientific AI, simulations, and data-intensive workloads across European academia and industry.


top500.org
Key Trends for AI
US dominance in raw power (top 3), focused on national labs for science + security.
top500.org
NVIDIA-heavy systems (e.g., JUPITER Booster, Alps, Eagle) often shine in practical AI training/inference due to CUDA ecosystem and lower-precision performance.
Many of these support hybrid AI+HPC workflows: using AI to accelerate simulations, surrogate models, or generative design.
This aligns well with Nvidia booth discussions — systems like JUPITER Booster and Alps showcase Grace Hopper/Blackwell-era platforms in real-world exascale AI. At the expo, you could ask how their platforms (DGX, Jetson, etc.) power or scale similar workloads. Let me know if you want details on any specific system!

Platforms
DGX Platform (their flagship AI supercomputer line):
(Blackwell-based) and newer desktop options. 
DGX Spark: A compact personal AI supercomputer (Grace Blackwell Superchip) models up to ~200B parameters
DGX Station and full DGX SuperPOD for

Jetson + Isaac Platform (edge/robotics/physical AI):
Compact, power-efficient modules (e.g., Jetson Thor with Blackwell) for deploying AI on robots, drones, autonomous machines, and industrial edge. Nvidia's big push into "physical AI" — robots that perceive, reason, and act in the real world.
nvidianews.nvidia.com
Omniverse:
The platform for building 3D worlds, digital twins, and collaborative simulation using OpenUSD and RTX tech. Critical for developing and testing physical AI (robots, factories, autonomous systems) before real-world deployment. "operating system for the metaverse/industrial digital twins."
nvidia.com
Blackwell Architecture


Next up: Vera Rubin platform (announced recently)—extreme codesign across multiple new chips (Rubin GPU, Vera CPU, new networking like NVLink 6, BlueField-4 DPU, etc.) for even better inference economics and massive-scale AI factories.
investor.nvidia.com CUDA: The foundational parallel computing platform and ecosystem. huge library of optimized tools, TensorRT, NeMo, NIM microservices for easy deployment, etc.).
Full-stack networking & infrastructure: NVLink (high-speed GPU interconnect), BlueField DPUs (smart NICs for data center offload), MGX modular architecture. This lets them build efficient "AI factories."
Software layer: NeMo (for LLMs), Triton, Run:ai, and agentic AI tools.

Autonplatforms – drive &======

 

Compueters continued

OFFICIAL========== are exascale systems (over 1 exaflop/s) and heavily used for AI workloads alongside traditional HPC simulations.
Top 10 Supercomputers (November 2025)
El Capitan (Lawrence Livermore National Lab, USA) 
Architecture: HPE Cray EX255a with AMD EPYC 4th Gen + Instinct MI300A accelerators. 
Performance: ~1.809 Exaflops (Rmax). 
Unique AI uses: Nuclear stockpile stewardship, advanced materials science, and large-scale AI for national security applications. Strong in mixed-precision AI and scientific


Frontier (Oak Ridge National Lab, USA) : Pioneering AI-driven science, including climate modeling, drug discovery, and fusion energy research. It excels at coupling traditional simulations with AI surrogates for faster insights.

Aurora (Argonne National Lab, USA) 
Unique AI uses: Leads in many AI-specific benchmarks (e.g., HPL-MxP). Used for AI-accelerated discovery in battery materials, drug design, protein folding, cosmology, and fusion. Strong emphasis on integrating AI with simulation and data analysis.

JUPITER Booster (Jülich Supercomputing Centre, Germany – EuroHPC) 
Architecture: BullSequana XH3000 with NVIDIA GH200 Grace Hopper Superchips. 
Performance: 1.000 Exaflops (first European exascale system). 
Focused on training large language/multimodal models for European languages, climate science, digital twins (e.g., human organs), quantum computing validation, and industrial AI. Highly energy-efficient and renewable-powered.
fz-juelich.de


Eagle (Microsoft Azure, USA) 
Architecture: NDv5 with NVIDIA H100 GPUs. 
Performance: ~561 Petaflops. 
Unique AI uses: Cloud-based AI model training and commercial/research workloads. Supports large-scale generative AI and hyperscale AI infrastructure.

 


HPC6 (Eni S.p.A., Italy) 
Architecture: HPE Cray EX with AMD Instinct MI250X. 
Performance: ~478 Petaflops. 
Unique AI uses: Energy sector applications — seismic imaging, reservoir simulation, and AI for oil/gas exploration and optimization

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Compare AI uses of top systems
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