Apple unveils new Apple Foundation Models: two on-device models, including a 20B-parameter multimodal model called AFM 3 Core Advanced, and three cloud models
Apple Machine Learning Research
Context & Ripple Effects
Apple’s model work has moved from the roughly 3B-parameter on-device language model and server-side model described with Apple Intelligence in 2024 to a developer framework granting direct access to its on-device model in 2025. The new lineup extends that arc with updated device and cloud tiers, including a 20B-parameter multimodal device model.
The announcement also follows Apple’s earlier MM1 multimodal research and reported changes to Apple Intelligence’s underlying architecture. It matters because Apple is treating models, device execution, cloud execution, and developer access as parts of one platform layer rather than as a single assistant feature.
First-order effects
Apple gains a broader internal model portfolio: two models for local execution and three for cloud use, with AFM 3 Core Advanced expanding the on-device tier to multimodal workloads.
Developers using Apple’s Foundation Models framework have a clearer path to build features around Apple’s on-device model capabilities, while Apple Intelligence can route work across device and server environments.
Second-order effects
App developers will need to decide which experiences can rely on Apple’s local model layer versus cloud-backed capabilities, making Apple’s framework and runtime constraints more consequential for product design.
A stronger on-device multimodal tier raises the competitive pressure on other platform vendors to offer comparable model access across their device operating systems and cloud services.
Third-order effects
If Apple continues pairing local models with cloud models and exposing the local layer to developers, foundation-model capabilities could become a more tightly integrated operating-system platform primitive rather than a service developers obtain solely from independent AI providers.
The durable strategic question is whether developers can build broadly portable AI features while taking advantage of device-specific model stacks; deeper platform integration could increase the value of Apple’s ecosystem but also increase dependence on its tooling.
The trend: This is part of the shift from standalone AI assistants toward hybrid device-and-cloud model platforms embedded in consumer operating systems and developer frameworks.
Apple's new foundation models are genuinely exciting. The standout is AFM 3 Core Advanced, a 20-billion (!) parameter model that runs entirely on-device. Read that again. 20-billion, on-device, iPhone 17 Pro. It pulls this off by keeping the full model in flash memory and [image]
Interesting approach from Apple They are storing the shared attention block in the DRAM While the FFN weights stay in NAND and are loaded in the DRAM, depending on the request Apple is facing 3 constraints - 1) Limited DRAM size 2) Large model size (20B params) 3) Slow NAND read
I'm curious whether Apple's FFN NAND-like approach reduces the mobile DRAM requirement needed for on-device AI. If so... why doesn't Nvidia use this kind of technology? Wouldn't that mean the 128GB in N1X is overkill? [image]
AFM Core Advanced is just for the iPhone 17 Pro, M3+ Mac, and M4+ iPad It's a sparse model, fully multimodal, and unlike any other on-device model AFM Core is for other devices, a dense on-device model
https://machinelearning.apple.com/ ... Instead of forcing the entire model into DRAM, the full model is stored in flash memory (NAND). Because NAND-to-DRAM bandwidth is too slow to swap weights token by token, as standard MoE models require, AFM 3 Core Advanced makes routing deci…
Apple is not squeezing a small model to be good, They're making a large model behave small! AFM 3 Core Advanced (Can I call it AFM 3 pro?) is a 20B parameter model that activates only 1-4B parameters at inference time, stored in flash and loaded into DRAM on demand. Not cloud.
Apple announced major upgrades to the Foundation Model Framework today, but the framework itself is not new: Apple has made its own proprietary, 3B LLM available through the Foundation Model Framework at last year's WWDC. What is new is that the FMF now serves as a model router
AFM Core Advanced on-device model running on A19 Pro is a sparse model. It's 20B parameters. It's fully Apple designed. It is an MoE but when it processes the prompt, it only loads the parameters needed and locks them in. If it's 20B parameters total, but on a specific
More on each of the new Apple Foundation Models AFM Core Advanced is likely the most impressive on-device model available https://machinelearning.apple.com/ ... [image]
You can use Apple's Foundation Models in apps: developer.apple.com/documentatio.... There are a bunch of updates to the base model which you can learn about at machinelearning.apple.com/research/ app.... We also released new Human Interface Guidelines for Gen AI: developer.apple.…
whoah this Apple tech is cool — they run a 20B model in-memory, but that's far too big to actually fit in memory, so they use a tiny classifier to select which experts to load, once per inference instead of per output token — machinelearning.apple.com/research/ int... [image…