Apple releases Core AI framework for on-device LLMs

Summary

Eliminates per-token cloud costs, removes server dependencies, and keeps user data local—streamlining inference pipelines for developers targeting Apple Silicon. Model specialization on first load trades one-time latency for cached subsequent runs, changing how you architect cold-start handling.

Why it matters

Eliminates per-token cloud costs, removes server dependencies, and keeps user data local—streamlining inference pipelines for developers targeting Apple Silicon. Model specialization on first load trades one-time latency for cached subsequent runs, changing how you architect cold-start handling.

Sources

1. 1.Core AI framework, the official successor to Core ML
2. 2.support for both custom-converted PyTorch models and pre-optimized open-source models
3. 3.unified architecture for deploying models ranging from compact 3B-parameter vision models to large-scale LLMs, including reasoning models with up to 70B-parameter reasoning models
4. 4.ensures user data privacy, zero server dependencies, and zero per-token cloud costs
5. 5.unified hardware access, allowing workloads to seamlessly run across the CPU, GPU, and Neural Engine under one API
6. 6.ahead-of-time (AOT) compilation, which shifts work off the user's device, yielding near-instant load times
7. 7.the first attempt to use a model may take significantly longer than subsequent runs, once the model has been already cached