Google Introduces Gemma 3 270M for Task-Specific AI Applications

Google has announced Gemma 3 270M, a compact 270-million parameter model intended for task-specific fine-tuning and efficient on-device deployment. The release follows the launch of Gemma 3, Gemma 3 QAT, and Gemma 3n, expanding the Gemma open model family that has surpassed 200 million downloads.

The company said the model is built for instruction-following and text structuring, while also being energy efficient. Internal testing on a Pixel 9 Pro SoC showed the INT4-quantised version consumed 0.75% of the battery across 25 conversations.

“Gemma 3 270M embodies the right tool for the job philosophy,” the company said in a blog post. “It’s a high-quality foundation model that follows instructions well out of the box, and its true power is unlocked through fine-tuning.”

The model contains 170 million embedding parameters to support a large 256k vocabulary and 100 million transformer parameters, making it suitable for handling rare tokens and domain-specific fine-tuning. Quantisation-Aware Training (QAT) checkpoints are also available, enabling deployment at INT4 precision with minimal performance impact.

Google noted that the model is not designed for complex conversational use cases but can be specialised for applications such as text classification, entity extraction, compliance checks, query routing, and creative writing. Developers can also run Gemma 3 270M entirely on-device, addressing privacy-sensitive use cases.

The company pointed to Adaptive ML’s work with SK Telecom as an example of the effectiveness of specialisation. By fine-tuning a Gemma 3 4B model for multilingual content moderation, Adaptive ML achieved performance that exceeded much larger proprietary models.

Gemma 3 270M is already being used in creative projects, such as a Bedtime Story Generator web app developed with Transformers.js, demonstrating offline, web-based deployment.

Google is releasing both pretrained and instruction-tuned checkpoints on Hugging Face, Ollama, Kaggle, LM Studio, and Docker. The model can also be tested on Vertex AI or run with inference tools, including llama.cpp, Gemma.cpp, LiteRT, Keras, and MLX. Fine-tuning is supported through Hugging Face, UnSloth, and JAX, with deployment options ranging from local environments to Google Cloud Run.

“The Gemmaverse is built on the idea that innovation comes in all sizes,” the company said. “With Gemma 3 270M, we’re empowering developers to build smarter, faster, and more efficient AI solutions.”

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