What is Local AI — And Why It Matters

18. Your Recommended Path

Chapter 18 of 20 · 18 min

A Framework for Getting Started

Based on everything in this course, here's a recommended path for beginners.

Phase 1: Setup (Day 1)

  1. Install Ollama (Chapter 8)
  2. Pull TinyLlama for testing
  3. Pull Llama 3.2 7B for real use
  4. Have your first conversation (Chapter 9)
  5. Try three different interfaces (Chapter 10)

Time commitment: 1-2 hours Deliverable: Working local AI on your machine

Phase 2: Calibration (Week 1)

  1. Use it for one real task per day (Chapter 15)
  2. Notice where it shines and where it struggles (Chapters 15-16)
  3. Try different parameters (temperature, system prompts)
  4. Establish baseline expectations for quality and speed

Time commitment: 30 minutes/day Deliverable: Intuition for what local AI does well

Phase 3: Integration (Weeks 2-4)

  1. Identify your top 3 use cases (Chapter 15)
  2. Set up appropriate interfaces for each (Chapter 10)
  3. Create custom system prompts for recurring tasks (Chapter 12)
  4. Evaluate: do you need a GPU upgrade? (Chapter 7)

Time commitment: 1-2 hours total Deliverable: Local AI integrated into your regular workflow

Phase 4: Optimization (Month 2+)

  1. Upgrade hardware if needed and if it makes sense
  2. Try larger models as you get more comfortable
  3. Explore advanced tools (Open Interpreter, custom integrations)
  4. Consider fine-tuning for specialized tasks

Time commitment: Ongoing, as needed Deliverable: Optimized setup for your specific needs

Decision Points

Should you buy a GPU?

Calculate your monthly cloud AI spend. If it's >$15/month, a $400 GPU pays for itself in under 2 years. If you use AI daily for real work, it probably makes sense.

Which model should you use?

Start with Llama 3.2 7B. It's the best balance of capability and accessibility. Upgrade to 13B or 70B when your hardware supports it and you have the need.

Should you pay for any services?

Some services are worth paying for:

  • Ollama is free and open-source
  • LM Studio has a free tier, paid tier adds features
  • Jan is free and open-source

No subscription is required. But if a tool saves you significant time, supporting the developers is reasonable.

EXERCISE

Write down your three highest-value local AI use cases. For each, estimate:

  • How often you'll use it (times per week)
  • How much time it saves (minutes per use)
  • Whether privacy matters (yes/no)

This gives you a concrete picture of what local AI is worth to you—and a baseline for evaluating progress.