Learnitweb

Introduction to Ollama

1. What Is Ollama?

Ollama is a lightweight runtime that allows you to run large language models (LLMs) locally on your own machine. Instead of calling cloud-based APIs (like OpenAI or Anthropic), Ollama enables you to download open-source models and perform inference completely offline.

In simple terms:

  • Ollama is to LLMs what Docker is to containers
  • It abstracts away the complexity of:
    • Model downloads
    • Model configuration
    • Runtime management
  • You interact with models using simple terminal commands

2. Why Ollama Exists

Most Generative AI tutorials today rely on paid cloud APIs, which introduce several limitations:

  • API keys are mandatory
  • Credit cards are required
  • Usage is metered and billed
  • Data leaves your system
  • Internet connectivity is compulsory

Ollama was created to solve these problems by making local-first LLM usage practical and easy.

3. Key Features of Ollama

3.1 Local Execution

  • Models run entirely on your machine
  • No external API calls
  • No data sent to third-party servers
  • Ideal for privacy-sensitive use cases

3.2 Open-Source Model Support

Ollama supports many popular open-source LLMs, including:

  • LLaMA 2 / LLaMA 3 – general-purpose reasoning and chat
  • Gemma / Gemma 2 – efficient models by Google
  • Mistral – strong instruction-following and reasoning
  • Code LLaMA – optimized for programming tasks
  • Phi-3 Mini – lightweight models for low-resource systems
  • Neural Chat – conversational fine-tuned models

Each model comes in multiple sizes (2B, 7B, 8B, etc.), allowing you to balance performance vs hardware capability.

3.3 Simple CLI-Based Interaction

Ollama is primarily used through the command line:

  • One command to download and run a model
  • Interactive chat-style interface
  • No complex configuration files required

Example:

ollama run llama3

This single command:

  • Downloads the model (if not already present)
  • Starts the model
  • Opens an interactive prompt

3.4 Automatic Model Management

Ollama handles:

  • Model downloads
  • Model caching
  • Versioning
  • Storage location

Once a model is downloaded:

  • It is reused automatically
  • No repeated downloads
  • Faster startup on subsequent runs

4. How Ollama Works Internally (Conceptual Overview)

At a high level, Ollama follows this flow:

  1. User issues a command
    Example: ollama run gemma:2b
  2. Model availability check
    • If the model exists locally → load it
    • If not → download it from Ollama’s model registry
  3. Model runtime initialization
    • Model weights are loaded into memory
    • Inference engine is started
  4. Prompt interaction loop
    • User enters text
    • Model generates tokens
    • Output is streamed back to the terminal
  5. Session termination
    • User exits
    • Model is unloaded from memory

All of this happens locally, without internet access once the model is downloaded.

5. System Requirements

5.1 Operating System

Ollama supports:

  • Windows 10 or later (Windows 11 recommended)
  • macOS
  • Linux

5.2 Hardware Considerations

  • RAM
    • 2B models: suitable for low-end laptops
    • 7B–8B models: require more memory
  • CPU / GPU
    • CPU-only execution is supported
    • GPU acceleration (if available) improves performance
  • Disk Space
    • Models range from a few GBs to 8+ GBs

Performance is directly tied to your system configuration.

6. Installing Ollama

Installation is intentionally simple:

  • Download the installer for your operating system
  • Run the installer
  • Follow standard installation steps
  • No environment variables or manual setup required

After installation:

  • Ollama runs as a background service
  • You do not need to start it manually
  • It is ready to accept commands immediately

7. Running Your First Model

7.1 Starting a Model

Open your terminal and run:

ollama run llama3

What happens:

  • If LLaMA 3 is not present, it is downloaded
  • If already present, it starts instantly
  • An interactive prompt appears

7.2 Interacting with the Model

Once running, you can:

  • Ask natural language questions
  • Request explanations
  • Generate summaries
  • Brainstorm ideas

Example:

Explain JVM in simple terms

The response is generated locally by the model.

To exit:

exit

8. Using Different Models

Switching models is trivial:

ollama run gemma:2b
ollama run mistral
ollama run codellama

Each model has different strengths:

  • Gemma – lightweight and efficient
  • Mistral – reasoning and instruction following
  • Code LLaMA – programming-focused responses

You can freely experiment without worrying about cost.

9. Ollama for Developers

Ollama is particularly useful for developers because:

  • No rate limits
  • Unlimited experimentation
  • Ideal for learning and tutorials
  • Works well with:
    • RAG (Retrieval-Augmented Generation)
    • Vector databases
    • Document-based Q&A systems
    • Local AI agents

It is commonly used alongside frameworks like LangChain and LlamaIndex (conceptually), but Ollama itself remains framework-agnostic.

10. Advantages of Ollama

  • Fully local execution
  • No API keys or billing
  • Strong privacy guarantees
  • Easy model switching
  • Beginner-friendly
  • Production-ready for internal tools

11. Limitations to Be Aware Of

  • Performance depends on your hardware
  • Large models require significant RAM
  • Cloud models may still outperform local models for very complex tasks
  • Initial model download can be large

These are trade-offs in exchange for cost-free, private, offline AI.

12. When Should You Use Ollama?

Ollama is ideal when:

  • You want to learn LLMs without spending money
  • You need offline or air-gapped AI
  • Data privacy is critical
  • You want full control over models
  • You are building prototypes or internal tools