Compact models are making private, responsive AI possible on more devices and specialized systems.
Size changes the deployment choices
A smaller model may run with less memory, energy and delay. That makes it practical for offline tools, limited hardware and tasks where data should remain within an organization.
Capability should match the task
Compact systems may perform very well within a narrow domain while lacking broad knowledge. Evaluation should focus on the intended workflow rather than a general leaderboard.
Local systems still need governance
Organizations must manage updates, testing, access and generated errors even when no external service is involved. Local control creates options, not automatic safety.