AI Edge Gallery

Why Large? Tiny LMs & Agents on Edge/Robotics — Cormac Brick, Google

Jul 25, 2026 · 21:45

Cormac Brick of Google AI Edge argues that the real constraint on edge AI is DRAM cost, not compute, making tiny models essential for widespread deployment. Small models (1–4 billion parameters) require 4–8 GB of RAM, but their quantized Gemma 2B model—at just 2.9 bits per weight—runs on a Raspberry Pi at 7.6 tokens per second and on a Qualcomm NPU at 31 tokens per second. For even lower-end devices, tiny models (50–500 million parameters) need under 2 GB of RAM and can be fine-tuned for specific tasks like voice-to-function calling, reaching over 86% reliability on ten actions. A shipped example is an offline voice dictation app that uses two fine-tuned sub-billion Gemma models to clean up ums and ahs without a subscription. The talk covers the Gemini-optimized toolchain (Lighter TLM, MediaPipe), synthetic data generation for fine-tuning, and the trade-off between zero-shot prompting in small models versus fine-tuning tiny models for reach and speed.

Prompt to Pipeline: Building with Google's Gen Media Stack — Paige & Guillaume, Google DeepMind

May 23, 2026 · 1:54:35

Paige Bailey, Guillaume Vernade, and Ian Valentine from Google DeepMind demonstrate the company's full generative media stack—from Gemini 3.1 Flash Light's video analysis at $0.25 per million tokens to Genie 3's playable world models and Gemma 4's on-device agentic coding—showing developers how to build multimodal apps without cloud APIs. Paige shows AI Studio's Build feature creating a bookshelf scanning app with Firestore and OAuth. Guillaume walks through a workshop using Nano Banana 2 for character portraits, VO 3.1 Lite for video generation at $0.05 per image, LIA 3 for chapter scores, and text-to-speech with distinct voices. Ian runs Gemma 4's 26B mixture-of-experts model on a MacBook to generate 10 SVGs in parallel and build a game from a spec, all locally. The episode argues that Google's models absorb common agent patterns, making custom fine-tunes less necessary.

Gemini Nano on device — Florina Muntenescu & Oli Gaymond, Google DeepMind

May 22, 2026 · 19:38

Florina Muntenescu and Oli Gaymond from Google DeepMind explain Gemini Nano, an on-device model that ships at 3–4 GB and is shared across apps via the AI Core system service, which handles scheduling, queuing background batch jobs overnight, and prioritizing foreground apps. The MLKit GenAI APIs (prompt API for text/image input, text output) give access to Gemini Nano, but require flagship devices from the last two years for optimal performance; classic MLKit (vision, OCR) runs on over a billion devices. Hybrid inference, launched weeks before this talk, automatically falls back to Gemini Flash in the cloud when the on-device model isn't available, extending reach. An embedding API for RAG-style solutions is coming soon. For fully custom models, LiteRT offers an alternative path but requires more developer effort for testing and optimization.