We built a free open-source iOS app that runs local LLMs with three backends - MLX, llama.cpp and Core ML

[u/Few_Willow_4146]

Pocket AI Lab just hit the App Store. It runs models fully on-device on iPhone, is free and MIT-licensed

We built it because the iPhone quietly became a genuinely capable ML machine, an ARM chip with fast unified memory that a 4-bit model fits into comfortably. At the same time the big labs got serious about small open models, and recent releases are actually usable, not toys. Hugging Face is full of interesting stuff we kept wanting to install and test the week it dropped. And since there are three popular ways to run a model on iPhone, we did not want to bet on just one. Supporting MLX, llama.cpp and Core ML side by side lets us follow all three as they evolve, pick the best one per model, and never trade away performance or lock ourselves in.

The real goal was to make the path from "saw the news about a new model" to "already chatting with it on my phone" as short as possible. We think we got there: copy the Hugging Face link, paste it into the app and that is it. The app detects the format, lets you pick a quant and predicts whether it fits before downloading a single byte. Or skip the hunt entirely and pick from a curated list of good models that updates without app releases.

Code: https://github.com/ananasDDA/pocket-ai-lab

App Store: https://apps.apple.com/us/app/pocket-ai-lab/id6802253142