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Ars Technica Jun 3, 2026 at 19:10 Big Tech Rising Hot

Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM

Gemma 4 12B uses a new encoding scheme and token prediction to punch above its weight.

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By Ryan Whitwam Original source
Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM

The generative AI boom has driven the cost of memory into the stratosphere, and Google is a key part of that trend. So it's only fitting that Google should offer some less RAM-hungry local AI models. The company has announced the release of a new Gemma 4 model that fills a gap in the lineup that launched earlier this year. The new model is efficient enough that you may be able to run it on a pretty average consumer laptop. In April, Google released four models in the Gemma 4 family, which also marked the shift to a more open Apache 2.0 license. The initial models included two mobile-optimized options (E2B and E4B) along with a pair of models for more serious work (26B Mixture of Experts and 31B Dense). That left a rather large unserved space in the middle, which is right where the new model falls. Gemma 4 12B is considerably more capable than the mobile versions, but it won't require a $20,000 AI accelerator to run locally. Google says Gemma 4 12B is unique in that it can run on many consumer laptops without sacrificing quality. As long as you've got a computer with 16GB of system RAM or VRAM, the 12-billion-parameter model will work. That's about half the total memory footprint of Gemma 4 26B MoE, and Google claims the new model is almost as capable, at least as far as benchmarks go. Read full article Comments

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Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM

Gemma 4 12B uses a new encoding scheme and token prediction to punch above its weight.

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SecurityLab Jun 6, 2026 at 10:02 Cybersecurity
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Звонит «мама», но это не она. Android теперь сбрасывает звонок мошенников раньше, чем вы успеете сказать «алло»

Google добавил в Android защиту от звонков с клонированным голосом.

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