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Google’s DeepMind AI research team has unveiled a new open source AI model today, Gemma 3 270M.
As its name would suggest, this is a 270-million-parameter model — far smaller than the 70 billion or more parameters of many frontier LLMs (parameters being the number of internal settings governing the model’s behavior).
While more parameters generally translates to a larger and more powerful model, Google’s focus with this is nearly the opposite: high-efficiency, giving developers a model small enough to run directly on smartphones and locally, without an internet connection, as shown in internal tests on a Pixel 9 Pro SoC.
Yet, the model is still capable of handling complex, domain-specific tasks and can be quickly fine-tuned in mere minutes to fit an enterprise or indie developer’s needs.
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On the social network X, Google DeepMind Staff AI Developer Relations Engineer Omar Sanseviero added that it Gemma 3 270M can also run directly in a user’s web browser, on a Raspberry Pi, and “in your toaster,” underscoring its ability to operate on very lightweight hardware.
Gemma 3 270M combines 170 million embedding parameters — thanks to a large 256k vocabulary capable of handling rare and specific tokens — with 100 million transformer block parameters.
According to Google, the architecture supports strong performance on instruction-following tasks right out of the box while staying small enough for rapid fine-tuning and deployment on devices with limited resources, including mobile hardware.
Gemma 3 270M inherits the architecture and pretraining of the larger Gemma 3 models, ensuring compatibility across the Gemma ecosystem. With documentation, fine-tuning recipes, and deployment guides available for tools like Hugging Face, UnSloth, and JAX, developers can move from experimentation to deployment quickly.
High scores on benchmarks for its size, and high hefficiency
On the IFEval benchmark, which measures a model’s abilit …