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.
