A hands-on Swift and Metal course for building LLM inference from first principles on Apple silicon, with 48 guided lessons, runnable exercises, a native macOS Studio, and a complete companion book.
Swift + Metal MoE inference for Apple Silicon: Gemma 4 26B in ~2 GB, Qwen 3.6 35B in ~1.45 GB, and DeepSeek-V4-Flash 284B at up to 4.8 tok/s on a 24 GB Mac. SSD-streamed experts, native Mac app, CLI, and OpenAI-compatible server.
Native LLM inference server for Apple Silicon. OpenAI + Anthropic API compatible. No Python. Includes MLX Core macOS app with chat, agent mode, and tool calling.
Swift SDK for running chat, vision and speech models on iPhone and Mac with Apple's Core AI. Model download and caching, FoundationModels integration, and runnable examples with documented OS, SDK and model requirements.