# On-device AI / edge AI

Record: term-on-device-ai · Type: term · Edition: 0.20.0 · Evidence cutoff: 2026-09-15

[Read in the atlas](https://theaiatlas.org/ideas/on-device-ai/) · [Complete evidence](https://theaiatlas.org/evidence.html#idea-on-device-ai) · [JSON](https://theaiatlas.org/records/term-on-device-ai.json) · [Pinned complete dataset](https://theaiatlas.org/editions/e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5/data.json)

Dataset pointer: `/glossary/132`. Reviewed: 2026-09-15.

> This is a curated, AI-assisted editorial atlas, not a census, affiliation classifier or independently fact-checked authority.

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## Publication dates and source age

Dated sources are over 18 months old; other dates are unknown.

Newest dated source: 2022-02-23. Assessed at this edition’s evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not establish validity or a new source-reading date. Unknown dates and month/year precision remain explicit in the JSON record.

## /summary

Running a model on a device such as a phone or laptop. Where a calculation runs does not by itself establish the privacy of the whole app.

Claim: claim-term-on-device-ai-1a5192ba3d7825ca26b24a72. Annotation: synthesis.

[glossary-wide-on-device](https://developers.google.com/edge/litert) · [glossary-wide-system-card](https://ai.meta.com/blog/system-cards-a-new-resource-for-understanding-how-ai-systems-work/)

## /definition

On-device AI runs model calculations locally. A model can be obtained or trained elsewhere, adapted for the target device and then run there. Edge AI is also used for processing near the source of data.

Claim: claim-term-on-device-ai-1e4c26398ee834b2e16dc7b8. Annotation: synthesis.

[glossary-wide-on-device](https://developers.google.com/edge/litert)

## /placement

Map context: this describes where computation happens. We do not treat local processing as a movement or a position on frontier pacing.

Claim: claim-term-on-device-ai-25f52a21ad9f2e54a62ed1b2. Annotation: editorial.

[glossary-wide-on-device](https://developers.google.com/edge/litert)

## /distinction

On-device inference is different from training on the device. To assess privacy, check the whole application’s data flows, including any other services it uses.

Claim: claim-term-on-device-ai-09422ce4d5c74cb753a9bb98. Annotation: synthesis.

[glossary-wide-on-device](https://developers.google.com/edge/litert) · [glossary-wide-system-card](https://ai.meta.com/blog/system-cards-a-new-resource-for-understanding-how-ai-systems-work/)

## Source provenance

### glossary-wide-on-device

[LiteRT: High-Performance On-Device Machine Learning Framework](https://developers.google.com/edge/litert)

Google for Developers · First-hand source (primary) · Published: undated · Updated: 2026-07-17 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read the platform overview and deployment workflow for running models on devices. The entry describes the location of computation, without adopting blanket vendor claims about privacy, speed or capability.

No archive check recorded.

### glossary-wide-system-card

[System Cards, a new resource for understanding how AI systems work](https://ai.meta.com/blog/system-cards-a-new-resource-for-understanding-how-ai-systems-work/)

Meta AI · First-hand source (primary) · Published: 2022-02-23 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read the system-card proposal, its distinction from model cards and its limitations section. Meta describes its own approach; publication of a card is not an independent audit of the system.

No archive check recorded.