Build a Local AI Dev Environment · 20 min · Docker · Ollama
The Local AI Stack — Design and Hardware Check
Picking a model size your hardware can't actually run well is the #1 way to decide local AI 'doesn't work' when the real problem was the model choice.
Hiring signal: Privacy, cost control, and offline capability are driving a real wave of local AI adoption -- knowing how to size a stack to real hardware (not just copy a tutorial's config) is what makes the difference between a working setup and a frustrating one.
What you will learn
- Explain quantization (Q4/Q8) and its effect on model size vs. quality
- Check your own hardware and determine what model sizes it can run
- Design your personal stack (which models, which RAM budget) before installing anything
Introduction
By the end of this course you'll have a complete private AI stack running on your own machine — chat, RAG over your own documents, and a code assistant in VS Code — with nothing sent to the cloud. Today, before installing anything: what can your specific hardware actually run well?
What You're Building
A hardware check, not a chatbot yet. This decides which model sizes the rest of the course should use for you — a decision every later lesson depends on getting right today.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Quantization: how a 16GB model fits in 8GB of RAM, What your hardware can actually run, What you're building today — plus a hands-on lab, quiz, and project artifact.
Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.
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