Running a large language model (LLM) on your computer is now easier than ever. You no longer need a cloud subscription or a massive server. With just your PC, you can run private and offline models like Llama, Mistral, or PHI.
This guide will show you how to install Open Source LLM locally, explain the tools involved, and walk you through both the UI and command line installation methods.
What we will cover
Understanding Open Source LLMS
An open-source large language model is a type of AI that can understand and generate text, much like ChatGPT, but it can work without depending on external servers.
You can download the model files, run them on your machine, and even Fine tune Them for your use cases.
Projects such as Llama 3, Mistral, Gemma, and PHI have made it possible to run models that fit well on users’ hardware. You can choose between smaller models that run on the CPU or larger ones that take advantage of the GPU.
Running these models locally gives you privacy, control and flexibility. It also helps developers integrate AI features into their applications without relying on cloud APIs.
Choosing a platform to run LLMS natively
To run an open source model, you need a platform that can load it, manage its parameters, and provide an interface to interact with it.
Three popular choices for local setups are:
Olma-A user-friendly system that runs models like OpenAIGPTOSS, Google Gema. It has both Windows UI and CLI versions.
LM Studio-A graphical desktop application for those who prefer a point and click interface.
GPT 4L – Another popular GUI desktop application.
We’ll use Olama as an example in this guide because it’s widely supported and easily integrated with other tools.
How to Install Ulama
Olama provides a one-click installer that sets up everything you need to run a local model. See Official Ulama websiteAnd download the Windows Installer.
Once downloaded, double-click the file to start the installation. A setup wizard will guide you through the process, which only takes a few minutes.
When the installation is finished, Ulama will run in the background as a local service. You can access it through its graphical desktop interface or using the command line.
After installing Olama, you can open the application from the Start menu. The UI makes it easy for beginners to start interacting with native models.
On the Olama interface, you’ll see a simple text box where you can type clues and receive responses. There is also a panel that lists the available models.
To download and use a model, select it from the list. Ulama will automatically fetch the model weights and load them into memory.
The first time you ask a question, it will download the model if it doesn’t exist. You can also choose model from model Model search page .
I will use Gemma 270 metersThe model which is the smallest model available in Ulama.
You can see the model downloading when used for the first time. Depending on the size of the model and the performance of your system, this may take a few minutes.
Once loaded, you can start chatting or running tasks directly within the UI. It’s designed to look and feel like a normal chat window, but everything runs natively on your computer.
You don’t need an internet connection after the model is downloaded.
How to Install and Run LLMS via Command Line
If you prefer more control, you can use the Olama Command Line Interface (CLI). This is useful for developers or those who want to integrate native models into scripts and workflows.
To open a command line, search for “Command Prompt” or “PowerShell” in Windows and run it. Now you can interact with Ulama using simple commands.
To check if the installation worked, type:
ollama --version
If you see a version number, Ulama is ready. Next, to run your first model, use the pull command:
ollama pull gemma3:270m
This will download the Gemma model to your machine.
When the process finishes, start with:
ollama run gemma3:270m
Ulama will launch the model and open an interactive prompt where you can type messages.
Everything happens locally, and your data never leaves your computer.
You can stop the model at any time by typing /bye.
How to manage models and resources
Each model you download takes up disk space and memory. Smaller models like the PHI-3 mini or Gemma 2b are lighter and suitable for most consumer laptops. Bigger ones like the Flawless 7B or Lallama 3 8b require more powerful GPUs or higher-end CPUs.
You can list all installed models using:
ollama list
And remove it when you no longer need it:
ollama rm model_name
If your computer has limited RAM, try running smaller models first. You can experiment with different ones to find the right balance between speed and accuracy.
How to use Olama with other applications
Once you install Olama, you can use it beyond the chat interface. Developers can connect to it using APIs and native ports.
Ulama runs a local server http://localhost:11434. This means you can send requests from your scripts or applications.
For example, a simple Python script might call a local model like this:
import requests, json
url = "
payload = {
"model": "gemma3:270m",
"prompt": "Write a short story about space exploration."
}
response = requests.post(url, json=payload, stream=True)
for line in response.iter_lines():
if line:
data = json.loads(line.decode("utf-8"))
if "response" in data:
print(data("response"), end="", flush=True)This setup turns your computer into a local AI engine. You can integrate it with chatbots, coding assistants, or automation tools without using external APIs.
Troubleshooting and common problems
If you encounter problems running the model, check your system resources first. Models need enough RAM and disk space to load properly. Closing other apps can help free up memory.
Sometimes, antivirus software can block local network ports. If Ulama fails to start, add it to the list of allowed programs.
If you use the CLI and see errors about GPU drivers, make sure your graphics drivers are up to date. Olama supports both CPU and GPU implementations, but the latest drivers improve performance.
Why running an LLM locally?
By doing LLMs locally you work with AI. You are no longer tied to API costs or rate limits. It’s ideal for developers who want to do rapid prototyping, researchers looking for fine-tuning, or hobbyists who value privacy.
Local models are also great for offline environments. You can quickly design, create content, or test AI-assisted apps without an Internet connection.
As hardware improves and open source communities grow, native AI is becoming more powerful and accessible.
The result
Setting up and running open source LLM on Windows is now easy. With tools like Olama and LM Studio, you can download a model, run it locally, and start generating text in minutes.
The UI makes it friendly for beginners, while the command line offers complete control for developers. Whether you’re building an app, testing ideas, or exploring AI for personal use, natively run models put everything in your hands, making it fast, private, and flexible.
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