Apple Can Create Smaller On-Device AI Models From Google's Gemini
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| Source: Mastodon | Original article
Apple has secured “complete access” to Google’s Gemini large‑language model inside Google’s own data centres, and is using that privilege to distill far smaller, on‑device versions for its products. The process—known as model distillation—feeds Gemini’s outputs and internal reasoning into a training pipeline that yields compact models capable of running on iPhone, iPad and other Apple hardware without a network connection.
The move matters because it gives Apple a shortcut to Gemini‑level performance while sidestepping the massive compute and memory footprints that typically accompany such models. On‑device AI can answer queries, translate speech and power context‑aware features with millisecond latency, lower battery drain and, crucially, keep user data out of the cloud. Apple’s ability to create proprietary derivatives also expands its control over the Siri experience, a point hinted at in our March 25 report that Apple may give Siri a “big AI overhaul” in iOS 27.
Distilling Gemini could accelerate Apple’s rollout of offline Siri functions, improve privacy‑first features in iOS 27 and bolster the company’s broader AI‑first narrative that pits its custom silicon against Nvidia’s H100‑based solutions highlighted in Google’s TurboQuant announcement earlier this month. It also deepens the strategic partnership between the two rivals, showing that Google is willing to share core model assets in exchange for Apple’s hardware expertise and market reach.
What to watch next: Apple has not disclosed a timeline, but integration is likely to appear in a beta of iOS 27 later this year. Developers will be keen to see whether Apple opens the distilled models through its Core ML framework, and regulators may scrutinise the data‑center access arrangement for antitrust implications. Benchmarks comparing the new on‑device models with the original Gemini and with Apple’s own internal models will provide the first concrete gauge of performance and privacy gains.
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