Ollama | Run AI on Your Own Computer — Understand Local AI, Test an Open Model, and Know When to Use the Cloud

Most people experience artificial intelligence through a

website or mobile app. You enter a question, the service processes it on remote computers, and a response appears on your screen.

But there is another approach: run an AI model on a computer you control.

This is part of the shift toward local AI and hybrid AI computing. Instead of sending every task to a cloud service, you can run certain models on your own hardware, keep some work offline after the necessary downloads, and experiment with AI tools without depending entirely on a hosted chatbot.

One practical starting point is Ollama.

This guide takes you from understanding local AI to running a model, testing its output, managing your downloads and deciding whether local or cloud computing makes more sense for a real task.

1. Understand what local AI actually means

An AI model is the trained computational system that produces outputs such as text, code or other supported content.

A model needs computing resources to run. Those resources can be provided by a remote data centre or by your own computer.

Cloud AI runs the model on infrastructure operated by a service provider. You access it through a website, app or API.

Local AI runs the model on your own device. Its speed depends on the model, available memory, processor, graphics hardware and software configuration.

Hybrid AI combines local and cloud computing. You might use a local model for simple, private or repetitive tasks and a cloud service for work that needs more capability.

These approaches have different trade-offs.

Consideration	Local AI	Cloud AI
Hardware	Depends on your computer	Mostly handled by the provider
Internet	Often needed for setup and downloads; some subsequent use can be offline	Usually needed
Privacy	Prompts can remain on your device when the entire workflow is genuinely local	Depends on the service, settings and data policies
Performance	Depends on available hardware and model size	Depends on the service, model and connection
Cost	Hardware, electricity, storage and setup time	May involve subscriptions, usage limits or API charges
Model choice	Depends on local software and hardware compatibility	Depends on the provider's available models

Local AI does not automatically make every activity private. If you connect a local model to a cloud API, online search, remote application or other external service, some information may leave your computer.

Likewise, a local model is not necessarily as capable as every cloud model. Choose the approach according to the task.

2. Meet Ollama

Ollama is software for running supported AI models locally and working with models through a command-line interface, an application interface and an API.

Official website: https://ollama.com/

It can help you:

• Download and run supported open models.
• Experiment with AI without building an entire model-serving system yourself.
• Test models for writing, summarization, coding and other suitable tasks.
• Connect local models to compatible development tools.
• Explore applications that use AI models through a local service.

Ollama also supports cloud models, so check which mode you are using before assuming a task is running entirely on your computer.

For this first exercise, the objective is modest: install Ollama, run one model and test it with a task whose output you can verify.

3. Check your computer before downloading a model

Do not start by downloading the largest model you can find.

AI models can occupy several gigabytes, and some require substantial memory and graphics resources to run comfortably. A model may download successfully but run slowly or fail to load if your computer lacks sufficient resources.

Before installation, check:

• Your operating system and whether it is supported.
• Available storage space.
• Installed memory (RAM).
• Your processor and graphics hardware, if applicable.
• Whether you can download several gigabytes of data.
• Whether you have permission to install software on the computer.

For example, Ollama's model library lists the gemma4:e2b model with a download footprint that varies by available variant and packaging. It is a smaller option within that model family, but it still requires meaningful storage and suitable hardware. Check the current listing before downloading.

Official model page: https://ollama.com/library/gemma4

If your computer has limited memory or storage, do not assume local AI is the best starting point. A browser-based AI service may be more practical because the provider supplies the computing resources.

4. Install Ollama

Use the official download page:

https://ollama.com/download

Choose the instructions for your operating system.

On Windows

1. Open the official download page.
2. Select the Windows download.
3. Download the installer and run it.
4. Follow the installation prompts.
5. Open the Ollama application if it does not start automatically.
6. Open PowerShell or another suitable terminal when you are ready to use the command-line interface.

The official Windows download page lists Windows 10 or later as a requirement; verify the current requirement before installing.

On macOS

1. Open the official download page.
2. Select the macOS download.
3. Download the application and follow the installation instructions.
4. Launch Ollama.
5. Open Terminal when you are ready to use commands.

On Linux

Follow the official Linux instructions on the download page. Installation methods and hardware acceleration depend on the distribution and hardware.

Important: Use official downloads. Avoid installers from unknown websites, and review commands before running them with elevated privileges.

5. Download and run your first model

Once Ollama is installed, open a terminal.

For this example, we will use the gemma4:e2b model identifier shown in Ollama's model library. Model names and variants can change, so confirm that the selected identifier is still available.

Enter:

ollama run gemma4:e2b

The first run will normally download the model if it is not already available locally. This can take time and use several gigabytes of data.

After the download and loading process completes, Ollama should provide an interactive prompt where you can enter a question.

Try:

Explain the difference between a website, a web server,
and a domain name using a simple example.

Read the response. Then ask a follow-up question to see whether the model can maintain the context of the conversation.

To finish the interactive session, use the exit instruction supported by the current interface, or close the session according to the application's instructions.

If the command fails, do not repeatedly download models or change random settings. Check the error message, model identifier, installation status, available storage and hardware compatibility first.

Official documentation: https://docs.ollama.com/

6. Test the model with a task you can verify

A model responding successfully does not prove that it is reliable. Your next step is to test it against a task where you already know what a good answer should contain.

For example, ask it to organize a short set of fictional business records:

Create a simple table with these columns:
Task, Priority, Next Action.

Use these tasks:
1. Correct a broken contact form.
2. Write a product description.
3. Review a website backup.

Assign a reasonable priority to each task.
Do not claim that any task has already been completed.

Check whether the model:

• Follows the requested format.
• Includes every task.
• Makes sensible recommendations.
• Avoids claiming that work has been performed when it has only described it.

Then test a writing task, such as asking it to simplify a paragraph you provide.

For code, test a small example and run it in an appropriate development environment. For factual questions, compare the answer with trustworthy sources.

A useful evaluation method is to test the same task with two models and compare accuracy, usefulness, speed, resource consumption and the amount of correction required.

Do not choose a model merely because its answer sounds confident.

7. Understand model size and performance

A model's size influences how much memory and storage it may require, but parameter count alone does not determine its quality or speed.

Other important factors include:

• Model architecture and quantization.
• Available system memory.
• Processor and GPU capabilities.
• Context length and the amount of information supplied.
• Software optimizations.
• The difficulty of the task.

Quantization is a technique that reduces the precision used to represent model parameters, often lowering memory requirements. Depending on the model and quantization method, this can involve a trade-off in output quality.

A smaller model may be practical for short summaries, simple explanations or routine text transformations. A more demanding task may benefit from a stronger model, more context or a cloud service.

The right question is not simply, “Which model is biggest?” It is, “Which model gives acceptable results for this task on the hardware I can actually use?”

8. Manage your downloaded models and storage

After experimenting, you may have several model files stored on your computer.

Ollama provides commands for managing them.

List the models available locally:

ollama list

To remove a model you no longer need, use its exact name from the list:

ollama rm gemma4:e2b

Only run the removal command if you intend to delete that local model. This does not necessarily remove the Ollama application or every other model on your computer.

Before deleting anything, make sure you do not need the model for another project. Downloading it again may consume data and time.

For additional commands and explanations, consult the official documentation: https://docs.ollama.com/

9. Know when to use local AI and when to use cloud AI

Local AI is most useful when it solves a real problem better than your alternatives.

Consider local execution when you want to experiment with models, build software that calls a local model, process appropriate data without sending prompts to a hosted AI provider, or reduce reliance on a particular cloud service.

Consider cloud AI when your computer is too limited, you need a capability unavailable in your local model, you want to avoid large downloads, or the provider's service is more practical for the task.

For some workflows, a combination works best.

Imagine you are developing a small application. You might use a local model for basic text classification or development experiments, then use a hosted model for a more demanding task. You would need to test quality and account for any API costs, privacy implications and operational requirements.

If you build a product around a model, also check the model's licence and permitted uses. “Open model” does not mean that every model has identical licensing terms or unrestricted commercial use.

10. Protect your data and your computer

Running a model locally is not a reason to ignore security.

Keep sensitive information out of untrusted integrations. A local model can be connected to tools, plugins, scripts or applications that behave differently from the model itself.

Be cautious with downloaded model files and extensions. Use trusted sources and check the relevant licence and documentation.

Understand network exposure. A local service intended for use on your own computer should not be exposed to other devices or the public internet without understanding authentication, firewall rules and access controls.

Do not grant unnecessary permissions. A model that can read files, execute code or control other applications creates additional risks beyond ordinary text generation.

Back up important work. AI experiments should not put your website files, customer records or business documents at risk.

Verify outputs before acting. Local models can still make up facts, misunderstand instructions, generate insecure code or produce misleading recommendations.

For consequential business, legal, financial, medical or safety decisions, use appropriate professional judgment and reliable evidence.

11. Turn the experiment into a useful project

Once you have confirmed that a model runs successfully, identify a small problem it could help solve.

Here are three possible projects:

A writing assistant: Build a simple workflow that turns rough notes into a structured draft. Review the output manually before publishing.

A website development helper: Test whether a model can explain a small code snippet, suggest a correction or generate a basic test case. Validate its suggestions before applying them.

A classification tool: Give the model a set of non-sensitive sample messages and ask it to sort them into predefined categories. Compare the results with your own expected labels.

Start with a narrow task. Define what success means, test the result, record errors and improve the workflow.

If you later connect Ollama to an application through its API, read the official API documentation first: https://docs.ollama.com/api/introduction

An API allows software to exchange requests and responses with a service. It is the bridge between experimenting with a model and incorporating it into a larger application.

Your practical assignment

If you have access to a compatible computer:

1. Check your operating system, storage and memory.
2. Install Ollama from its official website.
3. Select a model appropriate for your hardware.
4. Run one simple prompt.
5. Test the response against information you can verify.
6. Record the model used, task, result, speed and any errors.
7. Decide whether local execution offered a real advantage over a cloud-based alternative.

If you do not have suitable hardware, you can still learn the concepts and study the official model documentation without downloading anything. You do not need to buy a new computer simply to follow this lesson.

The bigger idea

Local AI is part of a broader shift in how computing resources are distributed. Some intelligence can run near the user, some remains in large cloud systems, and some applications combine both.

Understanding the difference helps you make better decisions about privacy, cost, performance, software design and infrastructure.

The goal is not to run every AI model locally. It is to understand the available options and select the right architecture for the problem you need to solve.

Official resources

• Ollama: https://ollama.com/
• Official downloads: https://ollama.com/download
• Ollama documentation: https://docs.ollama.com/
• Gemma 4 model library: https://ollama.com/library/gemma4
• Ollama API documentation: https://docs.ollama.com/api/introduction.  

Image: Machine Learning Mastery