Google developers blog unveils latest web, mobile, AI and cloud updates

Google’s new on-device AI capabilities mark a significant expansion in edge computing possibilities, bringing powerful language models directly to mobile devices and web applications. The introduction of Gemma 3n as Google’s first multimodal small language model, combined with new RAG and Function Calling libraries, provides developers with comprehensive tools to build sophisticated AI features that operate entirely on local devices without requiring cloud connectivity or compromising user privacy.

The big picture: Google AI Edge is dramatically expanding its on-device small language model (SLM) ecosystem with over a dozen new models, including the new Gemma 3 and multimodal Gemma 3n models, all hosted on the new LiteRT Hugging Face community.

• These models build upon Google’s initial four on-device models launched last year, now supporting Android, iOS, and Web platforms.
• Gemma 3n represents Google’s first multimodal on-device model, capable of processing text, images, video, and audio inputs without requiring cloud connectivity.

Key capabilities: The new Gemma 3 1B and Gemma 3n models deliver impressive performance in compact packages that can run on typical consumer devices.

• At just 529MB in size, these models can process up to 2,585 tokens per second during pre-fill operations when running on mobile GPUs.
• The models support a wide range of devices, making advanced AI capabilities accessible across different hardware configurations.
• Gemma 3n’s multimodal capabilities enable developers to create applications that can understand and respond to diverse input types beyond text.

New developer tools: Google has introduced complementary technologies that significantly enhance what developers can build with on-device language models.

• The new Retrieval Augmented Generation (RAG) library allows SLMs to access and leverage application-specific data without requiring fine-tuning.
• The Function Calling library enables language models to intelligently interact with predefined functions or APIs, making them more capable of completing practical tasks.
• These tools enable applications like natural language form-filling and intelligent data retrieval from large information sets, all while maintaining user privacy through on-device processing.

Why this matters: The expansion of on-device AI capabilities represents a fundamental shift in how AI applications can be designed and deployed, prioritizing privacy, reducing latency, and enabling offline functionality.

• By moving AI processing to the edge, applications can function without continuous internet connectivity while keeping sensitive user data local.
• The combination of multimodal capabilities with RAG and Function Calling creates possibilities for more contextually aware and interactive AI experiences.