Ollama Guide: How to Run Multiple Models + Model Names & Real-World Uses

Running Large Language Models (LLMs) locally is becoming easier with tools like Ollama. Whether you’re building AI apps, experimenting with models, or ensuring privacy, Ollama lets you run powerful models directly on your machine.

In this guide, you’ll learn:

  • How to install and run Ollama
  • How to run multiple models
  • Popular model names and their use cases
  • Best practices for developers

📌 What is Ollama?

Ollama is a tool that allows you to:

  • Run LLMs locally (no cloud needed)
  • Use models via CLI or REST API
  • Switch between multiple models easily

👉 Perfect for:

  • Offline AI apps
  • Secure environments
  • Fast local experimentation

⚙️ Step 1: Install Ollama

👉 For Linux / macOS:

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

👉 For Windows:

  • Download installer from official site

👉 Verify:

ollama --version

▶️ Step 2: Run Your First Model

ollama run llama3

👉 This will:

  • Download the model (first time)
  • Start an interactive chat

🔄 Running Multiple Models in Ollama

Yes—you can run multiple models, but:

👉 Important:

  • Models don’t run simultaneously by default
  • Each ollama run uses system resources (RAM/CPU/GPU)
  • You can switch models anytime

✅ Run Different Models

ollama run llama3
ollama run mistral
ollama run phi3

✅ List Installed Models

ollama list

✅ Remove a Model

ollama rm llama3

🌐 Using Ollama via REST API

Ollama exposes a local API:

POST http://localhost:11434/api/generate

Example:

{
  "model": "llama3",
  "prompt": "Explain multithreading in Java"
}

👉 Useful for:

  • Spring Boot apps
  • Node.js backends
  • AI agents

🤖 Popular Ollama Models + Uses

🧠 1. LLaMA 3

LLaMA 3

👉 Run:

ollama run llama3

✅ Best For:

  • General Q&A
  • Coding help
  • Content writing

💡 Example:

  • Blog generation
  • Interview questions
  • AI chatbots

⚡ 2. Mistral

Mistral

ollama run mistral

✅ Best For:

  • Fast responses
  • Lightweight systems
  • Low RAM machines

🧩 3. Phi-3 (Microsoft)

Phi-3

ollama run phi3

✅ Best For:

  • Edge devices
  • Mobile-like performance
  • Quick reasoning

💻 4. Code LLaMA

Code LLaMA

ollama run codellama

✅ Best For:

  • Writing code
  • Debugging
  • Explaining logic

🔍 5. LLaVA (Vision Model)

LLaVA

ollama run llava

✅ Best For:

  • Image understanding
  • Detect objects
  • Describe images

📚 6. Gemma (Google)

Gemma

ollama run gemma

✅ Best For:

  • Lightweight NLP tasks
  • Chat applications

🧪 7. Mixtral

Mixtral

ollama run mixtral

✅ Best For:

  • Advanced reasoning
  • Complex tasks
  • High-quality output

🧠 Choosing the Right Model

Use CaseBest Model
General chatbotLLaMA 3
Low resource systemPhi-3 / Mistral
Coding assistantCode LLaMA
Image understandingLLaVA
Advanced reasoningMixtral

⚡ Running Multiple Models (Best Practice)

👉 You can:

  • Keep multiple models installed
  • Switch based on use case

👉 For apps:

  • Use REST API → choose model dynamically

🧵 Example (Dynamic Model Selection)

String model = "llama3"; // or mistral / phi3

// send request to Ollama API

🚀 Real-World Use Cases

🧠 AI Chatbot

  • LLaMA 3 for conversation

💻 Coding Assistant

  • Code LLaMA for dev support

🖼️ Image Analyzer

  • LLaVA for vision tasks

📊 Data Processor

  • Mixtral for heavy reasoning

📱 Lightweight App

  • Phi-3 for mobile/edge

⚠️ Limitations

❌ High RAM usage
❌ Large model download size
❌ GPU recommended for performance

🎯 Conclusion

Ollama makes it incredibly easy to:

  • Run LLMs locally
  • Switch between models
  • Build AI-powered apps

👉 Start with:

  • llama3 for general use
  • mistral for performance
  • codellama for coding