Two years ago, running a useful model on your laptop meant 7B parameters of slow, hallucination-prone output. The math has changed. Llama 3.1 8B, Qwen 2.5, and Mistral Small now handle the same tier of tasks GPT-3.5 did in early 2023 — and they run on a MacBook Air with 16GB of RAM at usable speeds. The 70B-class models fit comfortably on a single high-end consumer GPU or an M-series Mac with 64GB+ unified memory, and they land somewhere between GPT-4-class and mid-tier Claude on most public benchmarks. This matters for one practical reason: "good enough" is no longer cloud-only. The Gap Closed Faster Than Anyone Expected If you spend $20-200/month on API calls for autocomplete, doc summarization, commit message generation, or local search, that budget now buys you something the local stack can approximate. A one-time hardware investment — or your existing laptop — replaces a recurring metered bill. The model-quality curve helps too. Open-weights releases used to lag the frontier by 18-24 months.…