Tagged small models · show all articles
Two questions decide how small and on-device AI matures by 2035: whether dedicated hardware closes the capability gap with the cloud, and whether on-device personalization makes privacy the default. Four scenarios, each with a falsifier.
A capable small model can be reached by compressing a large one, training a small one on purpose, searching architecture space for it, or routing around density altogether. These are four different engineering problems, not four contestants in one race.
A datacentre gives a model whatever it asks for. A device gives it a fixed envelope — memory, bandwidth, a sustained thermal budget, a battery — and a model has to be built to live inside it, not merely shrunk until it fits.