Running AI Locally: Privacy, Control & Offline Access
Learn to run powerful AI models on your own hardware. Cover Ollama, LM Studio, llama.cpp, GPU requirements, quantization, fine-tuning with LoRA, and building local AI applications.
5 lessons40 minutesFree
Start with lesson 1Lessons
Tips & Tricks
- Start with quantized models (Q4_K_M) — they run faster and use less RAM with minimal quality loss
- Ollama is the easiest way to get started — one command to download and run any model
- Check your VRAM first: 8GB runs 7B models, 16GB runs 13B, 24GB+ runs 70B quantized
- Use LM Studio's GUI to compare models side-by-side before committing to one
- For Apple Silicon Macs, use Metal-optimized builds — they run surprisingly well on M-series chips
- Set up a local API server with Ollama to drop local AI into any app that supports the OpenAI API format
Frequently Asked Questions
Is the "Running AI Locally: Privacy, Control & Offline Access" course free?
Yes. Every course on AI Model Wiki is free to read, with no payment or account needed.
How long does the "Running AI Locally: Privacy, Control & Offline Access" course take?
The listed estimate is 40 minutes across 5 lessons. You can work through them at your own pace and in any order.
What does this course cover?
Learn to run powerful AI models on your own hardware. Cover Ollama, LM Studio, llama.cpp, GPU requirements, quantization, fine-tuning with LoRA, and building local AI applications.
Do I need prior experience?
Some basic familiarity with AI Engineering concepts helps. This is an intermediate-level course.
Is my progress saved?
If you mark lessons complete, that is remembered in this browser on this device only. Clearing your browser data, or using another device, starts you from the beginning. There are no accounts or certificates for courses.