A look at Apple's technical approach to AI, including core model performance, alignment strategies, and adapter and on-device strategy
Apple Intelligence makes a lot of sense when you get out of the AI bubble. Plus, the cool technical details Apple shared about their language models “thinking different.”
Apple’s AI program is defined more concretely around a hybrid deployment model: core models can be adapted for specific tasks while some features run locally on devices.
The emphasis on alignment and model performance makes technical execution—not just feature demos—a central test for Apple Intelligence as it reaches Apple’s software platforms.
Second-order effects
A local-plus-cloud design raises the bar for rival device ecosystems: they must balance model capability with the device, latency, and privacy constraints of shipping AI inside existing products.
Developers integrating AI through Apple’s platform tools gain a clearer signal that task-specific, system-level features—not standalone chat interfaces—are the intended route to distribution.
Third-order effects
If this approach proves durable, consumer AI competition may increasingly split across hybrid architectures, with differentiation coming from integration, device capabilities, and operational controls as much as frontier-model scale.
The pattern supports a shift toward workflow-native AI, though the trade-offs between local quality, cloud capacity, and user expectations will determine how broadly that model holds.
The trend: Apple Intelligence is one data point in the move from standalone generative-AI products toward hybrid, operating-system-level AI embedded in everyday workflows.
“Apple's presentation rang very different than most AI keynotes we've seen in the last few years. While OpenAI and Google are trying to prove that they are the best at AI, Apple leaned into a narrative of what else we can do with AI. ” …
This is from Apple's State of the Union The local model is a 3B parameter SLM that uses adapters trained for each specific feature. Diffusion model does the same thing, adapter for each style. Anything running locally or Apple's Secure Cloud is an Apple model, not OpenAI. [video]
lot's of people wrote about Apple Intelligence, hopefully mine was the most in the weeds about the model details: - did Apple train a GPT4 level model? - why did Apple use another old RL algorithm MDPO for RLHF? - what it means for the open vs closed debates?
@natolambert I am somewhat doubtful they used the original MDPO algorithm in a token-level space. There are ways to frame online *PO as mirror descent, I would assume they uses some version of that in combination with the LOO approach from Cohere.
seems that Apple trained a “original GPT4"ish level performance model for servers. * Beating DBRX Instruct + Mistral 8x22b by a lot isn't super easy, but also not that hard with $$$ * Loses to current GPT4 Turbo, many models do The on-device model makes sense that they would... […
according to Apple's data, Apple's server-side foundational LLM appears to be insane, matching or beating GPT-4.5 in industry-standard benchmarks which really raises the question of why they even worked with OpenAI to begin with - server capacity? https://machinelearning.apple.co…
lol at Apple sneaking this RLHF gem into their “Apple foundation models” blog post and no one talked about it “We have developed two novel algorithms in post-training: (1) a rejection sampling fine-tuning algorithm with teacher committee, and (2) a reinforcement learning from...
I was grossed out when Apple got to image generation in their WWDC keynote, and I was grossed out when I read that Applebot scraped “the open web” to train their AI model, with publishers only being able to opt out after the fact. Disappointing to say the least. — https://www.…
MacStories suggests Apple is no better than others when it comes to AI training. I'd argue it's worse: after all, the creatives whose work is now being exploited are the people who kept Apple alive for so many years. — Disappointing. But also unsurprising. https://www.macstor…