The Chinese supercomputer LineShine was ranked as the fastest in the world, despite not using any GPUs. China now has the world's fastest supercomputer, overtaking the United States. The system, known as LineShine and installed at the National Supercomputing Center in Shenzhen, displaced the US system El Capitan from the top spot in the TOP500 ranking in terms of computing power. The breakthrough comes amid an intense competition between Beijing and Washington for technological supremacy, marked by high tariffs and restrictions on a wide range of hardware components and software. Since 1993, the TOP500 ranking has identified the world's most powerful supercomputers every six months through a series of standardized benchmarks that evaluate each system's performance, taking into account both its theoretical speed and its real-world performance, as well as its energy efficiency.

China beats U.S. with world's fastest supercomputer, but race not geared for AI work Workers at Elon Musk's xAI facility, which houses a large supercomputer known as Colossus, used for Artificial Intelligence (AI) data processing, in Memphis, Tennessee, on Sept. 11, 2025 | REUTERS SAN FRANCISCO - China has overtaken the U.S. to win the top spot on a list of the world's fastest supercomputers, but the results may say more about Beijing's desire to show self-sufficiency in computing systems than its standing in the global AI race, experts said. The LineShine system at the National Supercomputing Center in Shenzhen, China, uses domestically designed chips and won the top spot on the TOP500, a biannual global ranking of supercomputers, with the country's first listing in three years. The ranking comes as the U.S. and China are increasingly competing in advanced computing, with U.S. President Donald Trump on Monday signing an executive order that aims to put the U.S. ahead of China in the emerging field of quantum computing. In the June 2026 edition of TOP500, LineShine beat out the previous titleholder, El Capitan, a supercomputer housed at Lawrence Livermore National Laboratory that the U.S. government uses to develop and maintain its nuclear weapons stockpile. But technology and policy experts said the results do not mean that China has the world's fastest computer for AI work because of changes in the computing industry in recent years and the methods used to compile the list.

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In an unexpected turn, the two companies signed a deal for Anthropic to use computing resources from Elon Musk's xAI. Anthropic and Elon Musk's SpaceX said on Wednesday that the two entities have signed an agreement for Anthropic to use computing resources from xAI's data center in Memphis, Tennessee. It's the latest tie up in an industry that is scrambling to find enough computers to run complex AI software. SpaceX and xAI were previously separate companies, but the two merged earlier this year. The combined entity, also owned by Musk, is called SpaceXAI.

It generally takes 18 to 36 months to build a hyperscale AI site - such as, presumably, one of the world's most powerful supercomputers. It generally takes 18 to 36 months to build a hyperscale AI site - such as, presumably, one of the world's most powerful supercomputers. From press release to scrap metal site: the Essex'supercomputer' that's still a scaffolding yard Nscale's AI project still in use as depot ahead of pledged completion date - with planning permission filed after Guardian's inquiries Revealed: UK's multibillion AI drive is built on'phantom investments' T he press releases announcing a gleaming supercomputer on the outskirts of north London depict a glass and concrete building, rising from a tree-lined street. Accompanied by images of glowing blue robot faces, it looks like the centre of a technological revolution. By the end of this year, that artist's impression is supposed to be a reality.

We're about to simulate a human brain on a supercomputer The world's most powerful supercomputers can now run simulations of billions of neurons, and researchers hope such models will offer unprecedented insights into how our brains work What would it mean to simulate a human brain? Today's most powerful computing systems now contain enough computational firepower to run simulations of billions of neurons, comparable to the sophistication of real brains. We increasingly understand how these neurons are wired together, too, leading to brain simulations that researchers hope will reveal secrets of brain function that were previously hidden. Researchers have long tried to isolate specific parts of the brain, modelling smaller regions with a computer to explain particular brain functions. But "we have never been able to bring them all together into one place, into one larger brain model where we can check whether these ideas are at all consistent", says Markus Diesmann at the Jülich Research Centre in Germany.

Could 2026 be the year we start using quantum computers for chemistry? Whether quantum computers can actually solve practical problems is one of the biggest unanswered questions of this growing industry - and one that might be answered by researchers in industrial and medical chemistry in 2026. Calculating the structure, reactivity and other chemical properties of a molecule is an intrinsically quantum problem because it involves its electrons, which are quantum particles. But the more complex a molecule is, the harder these calculations become, in some cases posing a real challenge even for traditional supercomputers. On the other hand, because quantum computers are also intrinsically quantum, they should have an advantage when it comes to tackling these chemical calculations.

Google has announced plans to team up with the UK to invite researchers to come up with uses for the tech giant's state-of-the-art quantum chip Willow. It is one of several firms competing to develop a powerful quantum computer - which is seen as an exciting new frontier in the future of computing. Researchers hope they will be able to crack problems in fields such as chemistry and medicine which are impossible for current computers to solve. Professor Paul Stevenson of the University of Surrey - who had no involvement with the agreement - told the BBC it was great news for UK researchers. The collaboration between Google and the UK's national lab for quantum computing means more researchers will get access to the technology.

A vital ingredient for making quantum computers truly useful just might be conventional computers. That was the message from a gathering of researchers this month, which explained that classical computers are vital for controlling quantum computers, decoding the results of their calculations and even developing new techniques for manufacturing quantum computers in future. Quantum computers are made from qubits - quantum objects that may come in the form of extremely cold atoms or tiny superconducting circuits. The more qubits that a quantum computer has, the more computationally powerful it gets. But qubits are fragile, so they must be carefully calibrated, monitored and controlled.

Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters. We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations.