מדוע קטנים? מודלים זעירים וסוכנים ב-Edge/רובוטיקה

תקציר מקורי באנגליתThe constraint on edge AI is not compute, it is RAM, and it is getting worse: phone makers are shipping less of it this year, and a 6GB Raspberry Pi costs 2.5 times what it did at launch. So Cormac Brick's team at Google AI Edge spends its effort making models small enough to fit. A 2 billion parameter Gemma, quantized to 2.9 bits per weight, runs on a Raspberry Pi at about 8 tokens per second and on a Qualcomm NPU fast enough for a few frames of vision a second. Below that sit tiny models, from 500 million parameters down to 50, that reach the older laptops and cheap devices where even a small model will not fit. They usually need fine tuning rather than prompting, but the payoff is real: a fine tuned Gemma turns free text into the right function call across ten actions at over 86% reliab