“Local AI” describes an arrangement rather than a single product. A model may run entirely on a laptop, partly on a phone, or on a nearby workstation while the application keeps control of the interface. The important question is where data is processed and what must travel across a network.
Four trade-offs to map
Latency: A local response does not wait for a round trip to a remote service. This is useful for drafting, classification, and interactive controls where a short pause changes the experience.
Capacity: Smaller models fit within a device budget, but they may have less room for long context or complex reasoning. A useful test compares the smallest acceptable model with the task's real examples.
Privacy: Keeping input on a device can reduce exposure, but local processing is not automatically private. Logs, crash reports, sync services, and extensions still deserve an inventory.
Operations: A local model still needs updates, version tracking, evaluation, and a way to explain failures. Removing a hosted dependency moves work into the application team.
Local inference is a placement decision. It is not, by itself, a quality, privacy, or cost guarantee.
Build a small comparison
- Choose three representative tasks and record the expected output.
- Measure response time, memory pressure, and failure cases on the target device.
- Compare a local model with the hosted baseline using the same prompts and sources.
- Document what data is stored, where, for how long, and who can inspect it.
- Give the user a clear fallback when the local result is uncertain.
Sources and further reading
For terminology and constraints, see the Apple machine learning documentation, Google Gemma documentation, and the Hugging Face Transformers documentation. These links are references for readers, not endorsements.