[ implementation · developers ]
Make the boundary explicit, observable, and reversible.
A deployable architecture is more than a model runtime. Define the data path, capability fallback, device eligibility, update path, and failure behavior before asserting that an experience is local, private, fast, or resilient.
Implementation sequence
- Write the task contract. Define inputs, outputs, quality measures, unacceptable errors, user consent, and the human or product control that handles exceptions.
- Draw the data-flow and dependency map. Include device storage, inference runtime, network calls, logging, analytics, crash reporting, retrieval, content filters, identity, sync, and model delivery.
- Set a fleet policy. Establish supported devices and OS versions, minimum free storage and memory, accelerator availability, download behavior, thermal and battery constraints, and an alternative path for ineligible devices.
- Benchmark representative cases. Measure end-to-end latency, first-token or first-result time where relevant, throughput, quality, memory, battery or power, failure/retry behavior, and user-perceived completion—not a single best-device run.
- Ship controls with the model. Version prompts and models, make rollback possible, protect update integrity, minimize and document telemetry, enforce authorization at remote boundaries, and provide a user-visible failure state.
Local path
Use a bounded model or rule locally where it meets the contract. Report device eligibility and degradation honestly.
Remote path
Send only what the service needs after policy and user authorization. Treat provider controls as part of—not a replacement for—your system design.
Escalation path
Make remote escalation deliberate: identify the trigger, transferred fields, timeout, user notice where appropriate, and local fallback.
Do not publish unmeasured adjectives
“Private,” “secure,” “offline,” “low-latency,” and “sovereign” each hide conditions. State the processing boundary and tested conditions instead: for example, which input remains on device, which events still leave it, which devices qualify, and what happens without connectivity. Re-test on model, runtime, operating-system, or telemetry changes.
Sources and implementation references
- NIST Privacy Framework ↗
A risk-management framework for privacy; useful for mapping processing rather than treating location as a complete answer.
- NIST Cybersecurity Framework 2.0 ↗
A framework for managing cybersecurity risk across organizational functions.
- Google LiteRT documentation ↗
A current reference for cross-platform on-device model conversion, optimization, and deployment targets.
- Apple Core ML documentation ↗
A production framework reference for integrating models into Apple-platform applications.
[ MPS implementation library ]
Choose a boundary, then test it.
Decision framework
Compare privacy, capability, cost, latency, resilience, and device fit.
On-device vs cloud
The canonical workload-level deployment comparison.
Individuals
Choose a bounded, reversible personal workflow.
Schools
Plan classroom use with student-data and access boundaries.
Small organizations
Start with a managed, testable operational use case.
Developers
Build and measure a local, cloud, or hybrid boundary.
Glossary
Plain-language deployment and provenance terms.
Methodology & sources
What this library measures, assumes, and does not claim.