apple-on-device-ai

On-device AI on Apple Silicon runs offline by orchestrating Foundation Models, Core ML, MLX Swift, and llama.cpp for private, local inference. This combination enables tool calling and local execution without requiring network connectivity.

The best way to optimize LLMs for the Neural Engine is using coremltools for model conversion, applying quantization, palettization, and pruning. This workflow ensures the model is highly optimized for Core ML deployment and hardware acceleration on Apple devices.

You can use Foundation Models for tool calling in a local Swift app by initializing a LanguageModelSession and using @Generable with SystemLanguageModel. This enables structured, private, on-device AI workflows and multi-backend orchestration.

Yes, running GGUF models with llama.cpp is fully supported on Apple Silicon for high-throughput inference. The skill guides runtime orchestration across llama.cpp, MLX Swift, and Foundation Models to coordinate cross-backend operations.

Choose MLX Swift for running open-source LLMs with high-throughput inference on Apple Silicon, and use Core ML when you need Neural Engine acceleration via coremltools optimization. The skill helps coordinate these backends based on your specific workflow needs.

No, you do not need an active network connection to use SystemLanguageModel. By combining Foundation Models with local runtimes like MLX Swift and llama.cpp, the system delivers completely private, offline AI operation.