In plain words: Two language models were built to power Apple Intelligence: a small one that runs directly on phones and a larger one on private servers. The on-device model handles everyday tasks so data stays local, while the server model tackles harder jobs.
Abstract
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.
Tom Gunter, Zirui Wang, Chong Wang, Ruoming Pang, Andy Narayanan, Aonan Zhang, Bowen Zhang, Chen Chen, Chung-Cheng Chiu, David Qiu, Deepak Gopinath, Dian Ang Yap, et al.
arXiv:2407.21075 · cs.AI, cs.CL, cs.LG · submitted Jul 29, 2024 · updated May 27, 2026
abstract · pdf · html
> The AFM pre-training dataset consists of a diverse dataset consists of a diverse and high quality data mixture. This includes data we have licensed from publishers, curated publicly-available or open-sourced datasets, and publicly available information crawled by our web-crawler, Applebot. We respect the right of webpages to opt out of being crawled by Applebot, using standard robots.txt directives.
The fact that you can opt-out in robots.txt only if you knew to list Applebot months (years?) ago when they started crawling is a little unimpressive.