Bringing Gemma 4 12B to your Laptop: Unlocking Local, Agentic Workflows with Google AI Edge
Enabling efficient execution of large language models (12B parameters) on resource-constrained devices like laptops with limited RAM while maintaining multimodal and agentic capabilities.
Gemma 4 12B: The Developer Guide
Running high-performance multimodal AI models efficiently on consumer devices without the computational overhead of traditional visual and audio encoders.
Introducing the Google Colab CLI
Developers and AI agents needed a way to seamlessly execute code on remote GPU-powered Colab runtimes without context-switching between local terminals and web interfaces.
How the community trained Gemma to "Think" with Tunix and TPUs
How to enable developers with limited compute budgets to transform small base language models into capable reasoning engines through efficient training techniques.