Mistral Small 3 brings an open source to the fans-smaller, faster and cheapest

Bad artificial intelligenceEuropean artificial startup company revealed the rise quickly, which unveiled a new linguistic model today claiming to match the performance of the three -fold models with a significant reduction in computing costs – a development that can reshape the economies of advanced artificial intelligence.

The new model, is called Small Mistra 3It has 24 billion teachers and achieves 81 % accurately on standard standards with 150 symbols per second. The company issues it under leniency Apache 2.0 licenseAllow companies to modify and spread it freely.

“We think it’s the best model among all models with less than 70 billion teachers,” said Guillaume Lample, a major science employee at Mistral. “We are appreciated by an equal foot with Llama 3.3 70b Meta, which was released two months ago, which is a three -time model.”

This advertisement comes in the middle Intensive audit One of the costs of developing artificial intelligence after the Chinese start -up demands Deepseek has trained a competitive model Only 5.6 million dollars – The assurances that were wiped Nearly 600 billion dollars From the market value of NVIDIA this week, investors wondered about the huge investments made by American technology giants.

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Mistral approach focuses on efficiency instead of the range. The company achieved its performance gains primarily through improved training techniques instead of throwing more computing power in the problem.

“What changed is basically training improvement techniques,” Lampard told Venturebeat. “The way we train the model was a little different, a different way to improve it, and modify weights during free learning.”

The model was trained on 8 trillion symbols, compared to 15 trillion similar models, according to Landard. This efficiency can make AI’s advanced capabilities within the reach of the companies concerned with computing costs.

It is worth noting, Small Mistra 3 It has been developed without learning reinforcement or artificial training data, and technologies that are usually used by competitors. Lample said this “raw” approach helps avoid the inclusion of unwanted biases that may be difficult to discover later.

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The model specifically targets institutions that require local deployment for the reasons for privacy and reliability, including financial services, health care and manufacturing. It can be operated on a single graphics processing unit and dealing with 80-90 % of model use cases, according to the company.

“Many of our customers want an internal solution because they are concerned with privacy and reliability,” Lambel said. “They do not want critical services that rely on systems that are not fully controlled.”

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The version comes as bad, $ 6 billionHe placed himself as a European champion in the global artificial intelligence race. The company recently took Microsoft’s investments as it was preparing for Public subscription in the endAccording to CEO Arthur Minche.

Industry observers say Mestral’s concentration on smaller and most efficient models can prove that it ripens artificial intelligence industry. The approach contradicts companies like Openai and man It focused on developing large and increasingly expensive models.

“We are likely to see the same thing we saw in 2024, but perhaps more than this, it is basically a lot of open source models with very permitted licenses,” Lamples predicted. “We believe it is very likely that this conditional model will become a kind of commodity.”

With the intensification of competition and the gains of efficiency, the Mistral strategy can help improve smaller models to achieve democracy in reaching advanced artificial intelligence capabilities – which may accelerate the adoption through industries while reducing computer infrastructure costs.

The company says it will issue additional models with strengthening Thinking capabilities In the coming weeks, you can take an interesting test if his efficiency approach can continue to match the capabilities of much larger systems.

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