This isn't a hardware feat, this is a software triumph.

They didn't make special purpose hardware to run a model. They crafted a large model so that it could run on consumer hardware (a phone).

The software has real software engineers working on it instead of researchers.

Remember when people were arguing about whether to use mmap? What a ridiculous argument.

At some point someone will figure out how to tile the weights and the memory requirements will drop again.

Yes. I collected some details here: https://simonwillison.net/2026/Mar/18/llm-in-a-flash/

A similar approach was recently featured here: https://news.ycombinator.com/item?id=47476422 Though iPhone Pro has very limited RAM (12GB total) which you still need for the active part of the model. (Unless you want to use Intel Optane wearout-resistant storage, but that was power hungry and thus unsuitable to a mobile device.)

Looks like it's Qwen3.5-397B-A17B so 17B active. https://github.com/Anemll/flash-moe/tree/iOS-App

Apple is already one of the winners of the AI race. It’s making much more profit (ie it ain’t losing money) on AI off of ChatGPT, Claude, Grok (you would be surprised at how many incels pay to make AI generated porn videos) subscriptions through the App Store.

It’s only paying Google $1 billion a year for access to Gemini for Siri

Because someone managed to run LLM on an iPhone at unusable speed Apple won AI race? Yeah, sure.

I don't think we are ever going to win this. The general population loves being glazed way too much.

Better than waiting 7.5 million years to have a tell you the answer is 42.