Module 4: Ollama - Introduction

What will you learn in this module?

In Module 3 you used LM Studio (local GUI). Now you'll learn Ollama, a professional CLI tool for running LLMs locally in production.

Ollama vs LM Studio:

• ✅ CLI (vs GUI) → Automatable, CI/CD
• ✅ Docker support → Containerization, deploy
• ✅ Powerful model management → Pull, push, versioning
• ✅ Production-ready → Servers, clustering

By the end, you'll have Ollama running on your machine and you'll understand when to use CLI vs GUI.

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🎯 Module goal

Master Ollama: From CLI to production

Deliverables:

1. Ollama installed (Mac/Linux/Windows)
2. Models downloaded via CLI
3. A REST API running (OpenAI-compatible)
4. A chatbot with Ollama + Docker

Final project: Deploy a chatbot in Docker with Ollama

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📋 Module structure

Phase 1: Setup (capsules 1-3)

• 01: Module introduction
• 02: Installing the Ollama CLI
• 03: Model management (pull, list, remove)

Phase 2: Use (capsules 4-5)

• 04: Local REST API
• 05: Integration with Python

Phase 3: Production (capsules 6-7)

• 06: Docker deployment
• 07: Performance tuning

Phase 4: Project (capsule 8)

• 08: Mini-project - Chatbot in Docker

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🔗 Connections with other modules

Prerequisite:

• Module 3 (LM Studio): Concepts of local LLMs

Comparison:

• LM Studio: GUI, development, laptop
• Ollama: CLI, production, servers

Next:

• Module 5 (OpenRouter): Multi-provider cloud

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⏱️ Estimated time

Total: 3-4 hours

Breakdown:

• Setup: 30 min
• Model management: 30 min
• API integration: 1 hour
• Docker: 1 hour
• Project: 1 hour

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🛠️ Skills you'll develop

Technical:

1. Ollama CLI:

   • Basic commands (pull, run, list)
   • Model management
   • API server

2. Docker:

   • Containerizing Ollama
   • Docker Compose
   • Networking

3. Production:

   • Server deployment
   • Performance tuning
   • Monitoring

Conceptual:

• CLI vs GUI trade-offs
• Containerizing LLMs
• Production best practices

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📦 Prerequisites

Minimum hardware:

• RAM: 8GB (16GB recommended)
• Storage: 10GB free
• CPU: Any (GPU optional)

Software:

• macOS 11+, Linux, or Windows 10+
• Terminal access
• Docker (for capsules 06-08)

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💰 Cost

• Ollama: $0 (open source)
• Models: $0 (open source)
• Hardware: $0 (you use what you have)

Total: $0

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🎯 Ollama vs LM Studio vs OpenAI

Feature	Ollama	LM Studio	OpenAI
Interface	CLI	GUI	API
Difficulty	Medium	Low	Low
Production	✅	❌	✅
Docker	✅	❌	N/A
Cost	$0	$0	$$$

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📊 What you'll build

Final project:

A chatbot in a Docker container with:

• Ollama as the backend
• An exposed REST API
• Persistent storage
• Health checks
• Docker Compose setup

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✅ Success criteria

You completed it when:

• Ollama installed (ollama --version)
• Model downloaded (ollama pull mistral)
• API working (curl http://localhost:11434)
• Chatbot in Docker running
• You understand the CLI workflow

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🔗 Resources

1. Ollama.com - Official site
2. GitHub - Source code
3. Docker Hub - Official image

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🚀 Get started

Next: 02-installing-ollama.md

You'll install the Ollama CLI on your operating system.

Time: 15 min
Difficulty: Low

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Total time: 3-4 hours
Next: 02-installing-ollama.md