A practical guide to running local LLMs on macOS for experimentation and privacy
By
frontsideair
If you only eat one bagel today, this is the bagel.
Summary
A developer shares their skeptical yet practical guide to downloading and running large language models locally on macOS. The article covers the technical process of setting up and experimenting with local LLMs, emphasizing privacy benefits and the author's nuanced view that LLMs are neither mere autocomplete nor sentient beings, but something in between with emergent behaviors.
Key quotes
· 3 pulledSome call them fancy autocomplete, some argue that they are sentient and should have rights. The truth is somewhere in between.
Yes, they perform next word prediction, but it's so complex that there's nontrivial emergent behavior.
No, they don't have creativity or a mind.
You might also wanna read
Kuzco: Open-source Swift Package for On-Device LLMs on iOS and macOS
Kuzco is an open-source Swift package designed to integrate large language models (LLMs) into iOS, macOS, and Mac Catalyst apps. Built on `l
LLMTest: Automated LLM Model Selection and Fallback Tool for Developers
LLMTest is a tool created by maker Tom to help developers and "vibe coders" automatically select the best LLM models for AI-powered features
ModelHub: A macOS menu bar app for managing local LLMs across Ollama, MLX, and LM Studio
ModelHub is a native macOS menu bar app designed to streamline the workflow for developers working with local LLMs. It addresses the fragmen
Locally AI: Run AI Models Offline on Apple Devices
Locally AI is a software application that enables users to run various AI models (including Llama, Gemma, Qwen, and DeepSeek) locally on App
Unsloth: Open-Source Platform for Local AI Model Training and Inference
Unsloth is an open-source platform that enables users to run and train AI models and large language models (LLMs) locally on their own hardw
LLM Stats: Platform for Comparing AI Language Models by Benchmarks, Cost, and Capabilities
LLM Stats is a platform that allows users to compare various AI language models (LLMs) across multiple dimensions including performance benc
