Experimenting with Local LLMs on macOS (blog.6nok.org)

A detailed exploration of running large language models (LLMs) locally on macOS highlights the practical and ethical motivations behind this approach. While the author remains skeptical about LLMs’ sentience or creativity, they celebrate their utility for tasks like text summarization, journaling, and experimentation without relying on cloud-based AI services. Key reasons to run LLMs locally include enhanced privacy—especially for sensitive data—avoiding data harvesting by AI companies, and gaining firsthand technical experience with models outside the hype-driven commercial ecosystem. Technically, the article walks through two solid macOS-friendly LLM frameworks: the open-source llama.cpp runtime, installable via Nix and compatible with GGUF-quantized models like Gemma 3 (4B), and the more user-friendly but closed-source LM Studio app, which supports multiple runtimes including Apple’s MLX engine for faster inference. Important considerations include RAM constraints (models over ~12 GB strain 16 GB machines), model precision quantization (often at 4-bit for efficiency), and support for advanced features like reasoning, tool calls, and multi-modal inputs. LM Studio’s UI and plugin system enable sophisticated workflows such as running sandboxed code, web searches, and persistent memory on local tools like Obsidian. While local LLMs don’t yet match large online models in speed or accuracy, running them offers hands-on insight into architecture, trade-offs, and security, making them valuable for enthusiasts focused on privacy and machine learning literacy.