Introduction

Running AI models offline on your own computer is one of the most exciting advancements in modern AI. Thanks to lightweight frameworks like Ollama, you can now install, run, and interact with powerful Large Language Models (LLMs) locally — without needing cloud services or expensive GPUs.

Whether you’re a developer, researcher, content creator, or simply curious about AI, learning how to install LLMs locally using Ollama will open up endless possibilities. From privacy-focused workflows to faster experimentation and zero-cost operation, local AI is becoming a game-changer.

This ultimate easy guide will walk you step-by-step through installing Ollama, setting up your first LLM, and running AI models on Windows, macOS, or Linux — even if you’re a complete beginner.

What Is Ollama? (And Why You Need It)

Ollama is a lightweight, open-source framework designed to run LLMs locally on your computer. It takes care of:

• Model downloading
• Hardware optimization
• GPU/CPU acceleration
• Prompting and inference
• Model switching

Ollama supports many popular open-source models including:

• Llama 3
• Mistral
• Gemma
• Phi-3
• NeuralChat
• And dozens more

It’s fast, easy to use, and perfect for anyone wanting to experiment with local AI without complex setup.

System Requirements Before Installing Ollama

Before you install LLMs locally using Ollama, ensure you meet the minimum requirements.

Minimum Requirements

✔ 8GB RAM (16GB recommended for larger models)
✔ macOS, Windows 10/11, or Linux
✔ 5–20 GB free disk space (depending on model size)
✔ Optional GPU (for faster inference)

Supported Platforms

Ollama supports:

• macOS (Native support for Apple Silicon M1/M2/M3)
• Windows (Stable support as of 2024)
• Linux (Ubuntu, Debian, Arch, etc.)

How to Install LLMs Locally Using Ollama (Step-by-Step)

Below is the complete installation process for macOS, Windows, and Linux.

Step 1: Download and Install Ollama

For macOS

1. Visit the official website: https://ollama.com
2. Download the macOS installer (.pkg)
3. Open the package and complete installation
4. Open Terminal and type:

   ollama --version
   

   If you see a version number, installation was successful.

For Windows

1. Go to https://ollama.com/download
2. Download the Windows (.exe) installer
3. Run the installer and follow the setup
4. Open Command Prompt or PowerShell and enter:

   ollama --version
   

   If it returns the version, Ollama is ready.

For Linux

Run the following commands in your terminal:

Ubuntu / Debian

curl -fsSL https://ollama.com/install.sh | sh

Arch Linux

Use the AUR package:

yay -S ollama

Verify Installation

ollama run llama2

Step 2: Run Your First LLM Using Ollama

Once Ollama is installed, running your first model is extremely easy.

Example: Run Llama 3

ollama run llama3

Run Mistral

ollama run mistral

Run Gemma 2B

ollama run gemma:2b

Run Phi-3 (Small & Fast)

ollama run phi

Ollama will automatically download the model and launch it in an interactive chat mode.

Step 3: Use Ollama in Interactive Chat Mode

Once the model loads, simply type your prompts:

Example:

> Write a blog post outline about AI productivity tools.

The AI will respond instantly based on your hardware capabilities.

Step 4: Run Multiple Models and Switch Easily

Ollama allows you to run several models by simply switching commands.

List Installed Models

ollama list

Remove a Model

ollama rm modelname

Stop a Running Model

ollama stop modelname

Step 5: Create Your Own Custom LLM Model

One of Ollama’s best features is custom model creation using the Modelfile.

Example Modelfile:

FROM mistral
SYSTEM "You are an expert SEO writer."

Then run:

ollama create seo-model -f Modelfile

Run your custom model:

ollama run seo-model

This is great for:

• Personal assistants
• Writers
• Developers
• Automation workflows

Step 6: Connect Ollama With Other Applications

You can use Ollama models in other tools through API integration.

Example: Start the Ollama Server

ollama serve

Example: Use API Request

curl http://localhost:11434/api/generate -d '{
  "model": "llama3",
  "prompt": "Explain how LLMs work."
}'

Apps like:

• VS Code
• Obsidian
• Chat interfaces
• Automation tools
  can all connect to Ollama.

Step 7: Optimize Performance for Faster AI Responses

To improve speed when you install LLMs locally using Ollama, follow these tips:

✔ Use smaller models (2B–7B)

✔ Enable GPU acceleration

✔ Close background apps

✔ Increase system RAM

✔ Use quantized models (Q4, Q5, Q6)

Quantized models are lighter and faster with minimal quality loss.

Best LLMs to Run Locally on Ollama (2025–2026)

Here are the best-performing and most stable models:

⭐ 1. Llama 3

Best for conversation + coding.

⭐ 2. Mistral 7B

Fastest and most reliable.

⭐ 3. Gemma (by Google)

Lightweight and surprisingly powerful.

⭐ 4. Phi-3

Great for low-end PCs.

⭐ 5. NeuralChat

Good for writing tasks.

Choose based on your hardware and use case.

Troubleshooting When Using Ollama

❌ “Model fails to download”

Solution: Check internet or switch network.

❌ “Ollama command not found”

Solution: Restart your terminal OR reinstall.

❌ Runs too slow

Solution: Use a smaller model like phi or mistral:7b.

Conclusion

Learning how to install LLMs locally using Ollama is one of the simplest and most powerful ways to explore AI. With just a few commands, you can run advanced AI models, create your own custom assistants, and work offline — all without paying for cloud subscriptions.

Whether you’re a beginner or a tech enthusiast, Ollama makes running AI locally fast, secure, and incredibly flexible. Start with small models, experiment with prompts, and gradually build your own AI-powered workflow.

AI is now in your hands — right on your device.

FAQ

1. What is Ollama used for?

Ollama allows you to download and run LLMs locally on your computer.

2. Do I need a GPU to run LLMs locally?

No. GPU helps, but many models run well on CPU.

3. Is it free?

Yes, Ollama and most supported models are completely free.

4. Can I run multiple models?

Yes, you can easily switch or run several models.

5. Is it safe to run AI locally?

Yes — your data stays on your computer with full privacy.