While much of the AI world chases ever-larger models, Ravin Kumar (Google DeepMind) and his team build across the size spectrum, from billions of parameters down to this week’s release: Gemma 270M, the smallest member yet of the Gemma 3 open-weight family. At just 270 million parameters, a quarter the size of Gemma 1B, it’s designed for speed, efficiency, and fine-tuning.
We explore what makes 270M special, where it fits alongside its billion-parameter siblings, and why you might reach for it in production even if you think “small” means “just for experiments.”
We talk through:
- Where 270M fits into the Gemma 3 lineup — and why it exists
- On-device use cases where latency, privacy, and efficiency matter
- How smaller models open up rapid, targeted fine-tuning
- Running multiple models in parallel without heavyweight hardware
- Why “small” models might drive the next big wave of AI adoption
If you’ve ever wondered what you’d do with a model this size (or how to squeeze the most out of it) this episode will show you how small can punch far above its weight.
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