What is a local model? Execution location differs from open-source status
Explain device-local inference versus local interfaces to remote models, including dependencies, offline conditions and output checks.
Understand the concept
A local model generally runs inference on a user-controlled device. Local execution differs from open weights or open source, and installed software may call remote inference. Some prepared workflows run offline, but requests and tools need checks. Local operation guarantees neither correctness nor that all data stays on-device.
A local application need not use local inference
Whisper supports self-run speech recognition and Transformers documents downloads, caches and runtime setup. Such workflows load models on the device; desktop clients can instead send inputs remotely. Licensing governs use and distribution, not execution location. External searches or tools may still connect online, so verify the full workflow rather than calling it offline because weights exist.
Use a public sample to verify location and output
For a permitted recording, record model and device and compare transcription with audio. After preparation, test required functions offline where authorized. Identify remote stages instead of declaring a partial offline run complete. No model, cache or network inspection occurred here. Loading and task quality require separate validation.
Device resources and version maintenance remain necessary
Local deployment needs storage and runtime memory, which grow with context and concurrency. Replay outputs and compatibility after updates and retain working versions. Include hardware load and maintenance, not just the absence of per-call bills. Consider cloud services or smaller models when needed, with renewed quality checks. Preserve recoverable settings and sources; location is not a capability ranking.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.