Run an LLM Locally: A Privacy Setup Where Data Never Leaves the Machine
What this scenario solves
Sensitive data cannot go to a cloud API, yet you still need AI to process it — a seeming dead end.
Tool stack
Not the only solution, but a stack we have verified end to end. Each tool links to its full review, including who it is not for.
What you end up with
A local OpenAI-compatible endpoint your existing code can switch to by changing one base URL, with model and quantisation guidance.
Full steps
- 01安裝
- 02常用模型推薦
- 03基本使用
- 04API 模式
- 05在 Claude Code 中使用 Ollama
- 06實際使用場景
Articles carrying the full content
- Ollama: The Best Way to Run LLMs Locally
Ollama lets you run open source models like Llama, Mistral, and Qwen on Mac/Windows/Linux with one click. It is completely offline, free, and privacy safe.
2026-04-286 minRead - LM Studio Installation and Usage Guide: GUI Local LLM Management
Complete LM Studio guide: download, manage, and run local LLMs on Mac through a graphical interface, supporting GGUF and MLX formats and providing an OpenAI-compatible API.
2026-05-107 minRead - oMLX Installation and Usage Guide: Running MLX Models Natively on Apple Silicon
Complete oMLX installation and usage guide: running MLX format models natively on M3 Ultra, covering installation, model import, performance comparison, and optimal configuration.
2026-05-106 minRead - Enterprise Data Security Basics: What Data Should Never Be Fed to AI
Employees pasting customer personal data into free AI tools is one of the most common and hardest-to-prevent data leakage paths in enterprises. This article provides a four-tier data classification standard, a 'never upload' red-line checklist, a comparison of enterprise vs. consumer terms, local deployment options, and a question list for IT and legal teams.
2026-09-308 minRead
Adjacent scenarios
Other scenarios using
Level: Intermediate · Tracks: Developer Track · Business Track · Finance Track · Last verified: 2026-09-29