Cormac Brick
Principal Engineer at Google working on Google AI Edge and on-device machine learning, with public talks on deploying generative AI, tiny LLMs, and agents on edge devices.
RAM, Not Compute, Is Constraining Edge AI Deployment
Cormac Brick of Google AI Edge argues that RAM—not compute—is becoming the binding constraint on local AI, forcing developers to fit models within the memory budgets of phones, robots and low-cost devices. Models in the one-to-four-billion-parameter range can support prompt-based features on better-equipped hardware, he says, but broader deployment requires 50-million-to-500-million-parameter models fine-tuned for specific tasks. The trade is less generality for faster, cheaper offline capabilities such as voice control and dictation.
Fine-Tuning Pushed FunctionGemma From 46% to 90% Function-Calling Accuracy
Cormac Brick, a Google AI Edge engineer, argues that on-device agents are becoming practical when developers either use system models such as Gemini Nano through Android AI Core or ship narrow, fine-tuned tiny models with LiteRT-LM. His main example is FunctionGemma, a 270 million parameter function-calling model that rose from about 46% accuracy out of the box to more than 90% on most tested app-intent functions after synthetic-data fine-tuning. Brick presents the tradeoff plainly: system GenAI is easier when it fits, while app-shipped tiny models require more work but can run locally, offline, and with more control.