How to Run AI Models Locally with Python Using Ollama

Artificial Intelligence is becoming easier to use in everyday software projects. Many developers use cloud-based AI APIs, but there is another interesting option: running AI models directly on your own computer. This approach can be useful when you want to experiment with AI, build private applications, reduce dependency on external APIs, or learn how modern language models work.

Ollama makes this process much simpler. It allows developers to download and run supported AI models locally and interact with them through a simple command-line interface or API. Ollama can also be connected with Python, making it possible to build AI-powered applications without sending every prompt to a remote AI service.

In this tutorial, we will learn how to run AI models locally with Python using Ollama. We will cover installation, model downloading, Python setup, sending prompts, creating a simple chatbot, and some practical project ideas.

What Is Ollama?

Ollama is a tool designed for running AI models on a local computer. Instead of building the complete model infrastructure yourself, Ollama provides a convenient way to download models and communicate with them through commands and APIs.

It can be used with different types of models available through its model library. Ollama’s documentation also describes support for areas such as coding, vision, embeddings, and reasoning.

The basic idea is simple:

Your Python Application
        ↓
Python Ollama Library
        ↓
Ollama
        ↓
Local AI Model
        ↓
AI Response

This makes Ollama especially useful for students and developers who want to experiment with local AI applications.

Why Run AI Models Locally?

Running an AI model locally can be useful in several situations. You can experiment with models without creating an API integration for every small project. Local execution can also be helpful when working with information that you prefer to keep on your own machine.

Learn how LLM applications work
Build AI applications with Python
Experiment with different local models
Reduce dependency on external AI APIs for development
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Connect AI models with your own Python applications

However, local AI also depends heavily on your computer’s available RAM, storage, processor, and GPU capabilities. Larger models generally require more system resources.

Prerequisites

Before starting, you should have:

A Windows, macOS, or Linux computer supported by Ollama
Python 3.8 or newer
Ollama installed on your system
Enough storage for the model you want to use
Basic knowledge of Python
VS Code or another Python-compatible code editor

The official Ollama Python package currently requires Python 3.8 or newer.

Step 1: Install Ollama

First, install Ollama on your computer from its official website. After installation, Ollama can run as a local service that your applications can communicate with.

Once Ollama is installed, open Command Prompt, PowerShell, or your terminal and check that the command is available.

ollama --version

If the command displays an installed version, Ollama is ready to use.

Step 2: Download an AI Model

Ollama works with downloadable models. You need to pull a model before asking it questions from your Python program.

For example, you can use a model available in the Ollama model library:

ollama pull gemma4

The exact model you choose should depend on your computer’s resources and the type of application you want to build. Ollama’s current Python documentation uses gemma4 in its examples.

After downloading the model, you can test it directly from the terminal:

ollama run gemma4

Now you can type a question and see the model generate a response locally.

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Step 3: Create a Python Project

Open VS Code and create a new folder for your project. For example:

local-ai-python

Open the folder in VS Code and create a Python virtual environment:

python -m venv venv

Activate the environment on Windows:

venv\Scripts\activate

For macOS or Linux:

source venv/bin/activate

Using a virtual environment keeps your project dependencies separate from other Python projects.

Step 4: Install the Ollama Python Library

Now install the official Python package:

pip install ollama

The official Python client provides a convenient interface for communicating with Ollama from Python. It includes functions such as chat(), generate(), pull(), list(), and embed().

Step 5: Run Your First Local AI Program

Create a file named app.py and add the following code:

from ollama import chat

response = chat(
    model='gemma4',
    messages=[
        {
            'role': 'user',
            'content': 'Explain artificial intelligence in simple words.',
        }
    ],
)

print(response['message']['content'])

Run the program:

python app.py

Your Python program sends the prompt to the locally running Ollama model and prints the generated response. The official Python examples use the same basic chat() approach for communicating with a model.

How the Python Code Works

The first line imports the chat function:

from ollama import chat

The model parameter specifies which locally available model should process the request.

The messages parameter contains the conversation. The role tells Ollama who is sending the message. In this example, the role is user.

Finally, the generated content can be accessed using:

response['message']['content']

This structure follows the current Ollama Python client response format.

Using Generate Instead of Chat

For simple prompt-and-response tasks, you can also use the generate() function.

from ollama import generate

response = generate(
    model='gemma4',
    prompt='What is machine learning?'
)

print(response['response'])

The generate API is designed for producing a response from a model and prompt. Ollama’s API also supports options such as system instructions, output formatting, streaming, and other model parameters.

Build a Simple Python AI Chatbot

Once the basic connection works, we can create a small chatbot. This example repeatedly accepts questions until the user types exit.

from ollama import chat

print("Local AI Chatbot")
print("Type 'exit' to stop.")

while True:
    user_input = input("\nYou: ")

    if user_input.lower() == "exit":
        print("Goodbye!")
        break

    response = chat(
        model='gemma4',
        messages=[
            {
                'role': 'user',
                'content': user_input
            }
        ]
    )

    print("AI:", response['message']['content'])

This small program demonstrates an important concept: Python does not need to contain the AI model itself. It communicates with Ollama, which handles the model execution.

Maintaining Conversation History

A chatbot becomes more useful when previous messages are included in the conversation. Ollama’s chat API accepts a sequence of messages, which can be used to maintain conversation history.

from ollama import chat

messages = []

while True:
    user_input = input("You: ")

    if user_input.lower() == "exit":
        break

    messages.append({
        'role': 'user',
        'content': user_input
    })

    response = chat(
        model='gemma4',
        messages=messages
    )

    answer = response['message']['content']

    messages.append({
        'role': 'assistant',
        'content': answer
    })

    print("AI:", answer)

Here, both user messages and AI responses are stored in the messages list. This gives the model the conversation context during subsequent requests.

Streaming AI Responses

For applications that should display an answer gradually instead of waiting for the complete response, Ollama’s Python library supports streaming.

from ollama import chat

stream = chat(
    model='gemma4',
    messages=[
        {
            'role': 'user',
            'content': 'Explain Python in simple words.'
        }
    ],
    stream=True
)

for part in stream:
    print(part['message']['content'], end='', flush=True)

Streaming can make chatbot interfaces feel more responsive because generated content can be displayed as it arrives. The official Python client supports streaming for chat and generation requests.

Useful AI Projects You Can Build with Ollama and Python

After learning the basic connection, you can use Ollama as the AI component inside larger Python applications.

Local AI chatbot
AI-powered document assistant
Python coding assistant
Resume analysis application
Question-answering system
Local content summarizer
AI study assistant
Document classification system
AI-powered customer support prototype
Private knowledge-base application

Important Things to Keep in Mind

Local AI does not automatically mean every model will run quickly on every computer. Model size and system hardware have a major effect on performance. Before downloading a large model, check whether your computer has enough memory and storage.

You should also remember that a locally running model can still produce incorrect information. Local execution changes where the model runs, but it does not guarantee that every generated answer is accurate.

Conclusion

Running AI models locally with Python and Ollama is a practical way to start building AI applications without making the setup unnecessarily complicated. Ollama handles the local model runtime, while Python gives you the flexibility to create your own applications around it.

With just a few steps, you can install Ollama, download a model, install the Python library, send prompts, maintain conversations, and stream generated responses. Once these fundamentals are clear, you can move toward more advanced applications such as document assistants, coding tools, AI study systems, and private local chatbots.

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How to Run AI Models Locally with Python Using Ollama