Explained: How Apple trains it large language models without any data scraping

Apple

Apple has quietly entered the foundation-model race. In its newly released 2025 Tech Report, the company offers an unprecedented glimpse into the methods behind its AI systems—revealing a strategic blend of device‑first thinking, rigorous data sourcing, and advanced engineering.

Apple’s 2025 Tech Report reveals a carefully calibrated AI strategy: device-ready models, cloud-scale reasoning, extensive yet responsible data sourcing, and advanced architectural tweaks—all tightly aligned with the company’s privacy-first vision. As the AI arms race accelerates, Apple’s unique path may yield distinct advantages for users demanding both performance and protection.

Apple describes two flagship foundation models. The first is a compact ~3-billion‑parameter model tuned to run efficiently on Apple silicon within user devices. The second is a much larger mixture‑of‑experts (MoE) model designed for cloud deployment on Apple’s private servers, leveraging parallel‑track MoE architectures adapted for its infrastructure    .

Both aim to deliver advanced capabilities—multilingual text handling, image comprehension, and external tool-use—while preserving Apple’s hallmark privacy ethos  .

Training these models begins with vast, responsibly assembled corpora. Apple combines responsibly crawled multilingual and multimodal web data, licensed proprietary text, and image sources, and top-tier synthetic data produced in-house  .

Crucially, to safeguard privacy, Apple deliberately avoids using user data from personal devices. Instead, it relies on synthetic data generated by its internal models and anonymised metrics collected via opt‑in Device Analytics, underpinned by differential privacy techniques  .

A clever twist: Apple builds synthetic examples, such as draft emails or chat snippets. These are then compared—on-device—to opt-in user samples. Only the best synthetic matches are used for training, eliminating the need to harvest real personal content  .

Apple

For the on-device model, Apple introduces several innovations to meet efficiency and memory constraints. It deploys key‑value cache sharing, 2‑bit quantisation‑aware training, and a “Block 2” design that omits certain projection layers to achieve a 38 % reduction in memory and faster inference  .

These optimisations enable the model to perform advanced functions—summaries, context-aware reply suggestions, image-aware responses—all locally, without cloud computation.

Server‑side, Apple’s larger model relies on its custom Parallel‑Track Mixture‑of‑Experts transformer. MoE architecture dynamically routes different inputs to specialized expert subnetworks. Combined with Apple’s secure private‑cloud, this setup delivers cloud-class reasoning and multilingual understanding—while remaining under Apple’s full control   .

Apple’s training begins with self-supervised pre‑training on massive text and image-text corpora. Next comes supervised fine‑tuning on labeled tasks. Finally, both models undergo reinforcement learning from human feedback (RLHF) via an asynchronous training platform   .

This layered approach mirrors industry best practices, but Apple emphasises privacy-safe data and device‑first deployment.

Post‑training, sophisticated inference strategies are applied. The on‑device model uses quantisation‑aware compression and KV-cache sharing to run natively and efficiently. The server model exploits expert-level routing in secure private-cloud.

Together, they deliver fast, intelligent features—whether powering Siri-like tasks, summarization, or multimodal interactions—without compromising user safety or degrading user experience   .

Apple says its models support 15 languages and show improved tool-use, image understanding, and reasoning on par with industry standards. While no concrete benchmark scores are released, the report positions its lineup as competitive with leading public and private models.

Perhaps Apple’s boldest message: powerful AI and strong privacy are not mutually exclusive. Through innovations around synthetic data, on‑device processing, and analytics with differential privacy, the company argues privacy-first AI is both achievable and scalable.