I Ran Local AI on My MacBook and iPhone. The Gap Is Closing Fast

Running local LLMs on consumer hardware is now practical for many tasks. Using MLX on a MacBook Pro M3, Qwen 2.5 32B runs at usable speeds for code generation and analysis. On iPhone, smaller models (7B-14B) handle text classification and summarization well. The gap between local and cloud models is closing faster than expected, especially for structured tasks. The tradeoff is clear: local models give you privacy, zero latency, and no API costs, but they cannot match frontier models on complex reasoning. For an AI agent architecture, the sweet spot is using local models for routine tasks and cloud models for complex decisions.