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FOUNDATION MODELS VS CORE ML / CORE AI VS MLX

Foundation Models vs Core ML / Core AI vs MLX vs FastVLM

Three of these are frameworks and one is a model, which is exactly why they get confused. The Foundation Models framework hands you Apple’s model through a session API. Core ML—and from WWDC26, Core AI—runs models you convert and ship. MLX is an array framework for training and running open models on Apple Silicon. FastVLM is an open model you can run through any of the last three.

All comparisons
FeatureFoundation Models frameworkCore ML → Core AIMLXFastVLM (model)
What it isSwift/Python API over Apple’s built-in models (and, since WWDC26, any LanguageModel provider)Runtime for models you convert; Core AI is the WWDC26 successor built for generative workloadsOpen-source array framework (Python, Swift, C++) for Apple SiliconApple research VLM with open weights (0.5B–7B)
Model choiceApple’s AFM 3 Core by default; PCC, MLX, Core AI or cloud providers via the protocolAny model you convert from PyTorchAny open model with an MLX implementation (mlx-lm, mlx-vlm)Fixed: FastVLM-0.5B / 1.5B / 7B
What you shipNothing—the model is part of the OSCompiled model package inside your appWeights (downloaded or bundled) plus the MLX runtimeWeights via the runtime you choose
Runs onApple Intelligence devices; macOS for Python/CLIiPhone, iPad, Mac, Vision Pro, Apple TV, WatchApple Silicon Macs; iOS through MLX SwiftBrowser (WebGPU), Python/CUDA, Mac (MLX), iPhone (MLX/Core ML)
Vision inputImages since WWDC26; Vision framework tools callable by the modelWhatever the converted model supportsWhatever the model supports (mlx-vlm covers FastVLM)Yes—its whole purpose
Best forFast integration, structured output, no weights to manageCustom or fine-tuned models with full control and ANE/GPU accelerationResearch, fine-tuning and running open LLMs/VLMs on a MacHigh-resolution image Q&A with low first-token latency

Checked 2026-09-17

Decision summary

  • Start with the Foundation Models framework if you only need Apple-platform apps and the built-in model is good enough.
  • Reach for Core AI (or Core ML on older toolchains) when you must ship a specific converted model with full control.
  • Use MLX for experiments and open models on a Mac; FastVLM has an official MLX app.
  • FastVLM is not an alternative to these frameworks—it is a model that runs through them, or in the browser.

Pick the framework by constraint

  • No model management, Apple-only → Foundation Models framework
  • Custom model, on-device, full control → Core AI / Core ML
  • Mac research, fine-tuning, open weights → MLX
  • Web or Linux included → open model such as FastVLM via Transformers or Transformers.js

How to read this table

Core AI was announced at WWDC26 as the successor to Core ML; availability depends on the OS and Xcode versions you target. Check Apple’s documentation for the exact deployment targets before choosing.

Questions

Can I run FastVLM through the Foundation Models framework?

Since WWDC26 the framework accepts any provider that conforms to LanguageModel, including MLXLanguageModel. Whether a vision model like FastVLM is exposed that way depends on the provider implementation; the documented FastVLM paths remain MLX, Core ML, Transformers and the browser.

Is Core ML deprecated?

Apple positions Core AI as the successor for new generative workloads, but existing Core ML models and APIs continue to work on the platforms they shipped for. Plan new projects on Core AI and migrate existing ones on your own schedule.