Spent some time digging into Apple's new Core AI framework, and I think it's much more important than it initially appears At first glance, it looks like "just another inference framework." It's not !! Core AI is essentially Apple's production stack for running AI models on Apple Silicon, and it's the same framework powering Apple Intelligence A few things stood out: - Models can be developed in PyTorch and converted directly into Core AI models - Inference automatically utilizes CPU, GPU, and Neural Engine without developers manually orchestrating hardware - Core AI introduces first-class support for model states, which is particularly interesting for transformer workloads Another interesting concept is model specialization. The model you ship isn't the exact model that ultimately runs on the user's device Core AI ships a portable representation, then specializes it for the specific hardware and OS version. The specialized artifact is cached, which explains why the first load may take time but subsequent loads are much faster Apple also exposes APIs to: - Trigger specialization ahead of time - Inspect specialization caches - Share caches across app groups - Manage cache persistence This feels very similar to how serious inference systems think about compilation and deployment rather than just model execution The other thing I found interesting is how Core AI fits into Apple's broader AI stack MLX helps you train, fine-tune, and experiment with models Core AI helps you deploy, optimize, specialize, debug, and run them efficiently on Apple devices That's a much cleaner separation than I initially understood The biggest takeaway for me is that Apple seems less focused on winning the "best model" race and more focused on building the infrastructure required to run AI locally at scale And after looking at features like state management, specialization, ahead-of-time compilation, caching, dedicated profiling tools, and Metal-backed custom kernels, it's clear they're investing heavily in that layer