NederlandsComputing power
Computing power is how much calculation computers can do in a given time. For AI it mainly comes from large numbers of specialised chips in data centres; more computing power makes larger and better models possible.
Training or using an AI model is, at heart, a huge amount of arithmetic: multiplying and adding numbers, countless times in a row. How much of that work a computer, or an entire data centre, can handle in a given time is called computing power.
For AI, most computing power comes from specialised chips, such as GPUs, that perform many calculations at once. Thousands of those chips together in a data centre provide the computing power to train the largest models. That also takes a lot of electricity, which is why computing power and energy often go together in AI news.
Why it matters
In recent years AI models improved mainly because more data and more computing power went into training. That makes computing power one of the most important raw materials of progress in AI, and companies are investing billions in it.
Yet more computing power is not the only way forward. The research organisation METR estimates that Anthropic is now improving its models about twice as fast, and found convincing evidence that this is not because more computing power is going into training more quickly. AI itself is probably helping with the research. Anthropic also looked at how it divides its own computing power: about 6 per cent of the computing power for AI research went to safety work.
Articles about Computing power
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Tencent lets AI build its own tools for protein research and mathematics
Tencent has presented the second version of its research AI, Hyra. It delivers not only solutions but also tools that can be used again later. Models for proteins and molecules are already public, so other researchers can now test them themselves.
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Newest AI model can read the inside of other AI and write it itself
Researchers at MIT had OpenAI's GPT-6 Astra read and adjust the numbers that small AI networks are made of, without any training. It even wrote networks that play a computer game well. The researchers call these very early results.
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Meituan releases LongCat-2.5: a multimodal agent model with 1.6 trillion parameters, trained on Chinese chips
On 2 November the Chinese company Meituan released LongCat-2.5, a large AI model that handles text and images and can carry out long programming tasks on its own. According to Meituan, it was trained entirely on Chinese chips and comes close to Claude Opus 5 in tests.
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Rocky Linux creator builds Linux-compatible kernel in eleven days with an AI agent
Gregory Kurtzer, known for CentOS and Rocky Linux, built Ginux in eleven days: a new core for an operating system that can run existing Linux programs. An OpenAI AI agent wrote almost all the code. It is still a research prototype.
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Mistral releases a giant AI model for anyone to use, strong in coding and security
France's Mistral is putting Mistral Large 4, nicknamed Le Chonk, online for free: a model with a trillion parameters that anyone can download and, with enough computers, run themselves. According to the company, it is the best open model for programming and for securing software.
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GitHub adds 4 million CPU cores to cope with a wave of AI agents
In September, changes to code were saved 7.4 billion times on GitHub, more than five times as often as a year earlier, mostly because of AI agents. The platform added 4 million processor cores this year and is rebuilding its storage.
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Nvidia releases free speech model that transcribes recordings in 40 languages, translates them and recognises speakers
Nvidia has released an open speech model that transcribes, translates and shows who is speaking, all in one go. It uses only a small part of its computing power per step and runs on a single graphics card, without the cloud. For now, the performance figures come from Nvidia itself.
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SpaceX leases 40 billion dollars' worth of AI chips
SpaceX has signed a binding commitment to lease about 40 billion dollars' worth of AI chips and data centre equipment, including Nvidia's new Vera Rubin chips. The first equipment is due in the first half of 2027.
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What mathematicians think of OpenAI's 372 maths results
In early October, OpenAI put 372 results on open maths questions online. Mathematicians are impressed, but checking them will take months. What has been claimed, and what do experts think?
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Free tools take the brakes off Nvidia graphics cards: up to twice as much AI computing power
Free open-source tools remove a brake that Nvidia built into the firmware of its GeForce graphics cards. For AI calculations, some cards become up to twice as fast. In practice the gain is smaller, and the tools are still experimental.
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AI model designs experiments better than 24 experienced AI researchers
On a new test by P-Zero Research, Claude Opus 5.5 designs better AI experiments than the best of 24 human experts. The model needs less than half the computing time to do so and costs about one thirtieth as much.
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10-dollar light sensor from Cambridge can recognise substances in skin, food and plastic
Researchers at the University of Cambridge made a spectrometer on a chip of a few centimetres, for about 10 dollars. It recognises substances by the light they absorb. In one volunteer it tracked blood sugar without a finger prick; the next step is testing in more people.
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AI model trained on almost four million fossils sheds new light on the great mass extinctions
Chinese researchers trained an AI model on almost four million fossils. It maps the history of life in unprecedented detail and suggests that three of the five great mass extinctions happened more gradually than thought. Other palaeontologists are somewhat more cautious.
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AI model Maxwell makes existing software faster while it runs
The Israeli company doubleAI presents Maxwell, an AI that rewrites programs while they run so that they go faster, without touching the source code. According to the company, one customer's computing costs fell by 67 per cent.
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d-Matrix splits the work of an AI model across two kinds of chips for faster answers
At a major data centre conference, the American chip company d-Matrix showed how an AI model answers faster when ordinary graphics chips and its own chips each do the work they are good at. The company now also supplies complete computing clusters.
