Run AI Locally on Windows 11

If you’ve ever wanted to run powerful AI tools without relying on the cloud, this guide on how to Run AI Locally on Windows 11 will walk you through every critical step. Whether you’re a developer, data scientist, or simply curious about trying large language models like Meta’s LLaMA 3 directly on your PC, setting up an offline AI environment using Ollama with WSL2 can transform how you work. It improves performance, enhances data privacy, and reduces operational costs quickly.

Key Takeaways

Windows 11 supports local AI development using the Windows Subsystem for Linux (WSL) and tools like Ollama and Docker.
Ollama lets you run large language models like LLaMA 3 completely offline using simple commands.
This guide includes hardware and system tips to help beginners choose the right configuration.
Running AI locally is faster, more private, and often more cost-efficient than using cloud-based APIs or platforms.

Also Read: Mastering Your Own LLM: A Step-by-Step Guide

Run AI Locally on Windows 11
Key Takeaways
Why Run Local AI Models on Windows 11?
Prerequisites and Suggested Hardware
Step-by-Step: Install and Setup WSL2
Install Docker for Windows with WSL Integration
Install Ollama and Run Your First AI Model
Tips for Performance and Optimization
Alternatives to Ollama
FAQs: Running AI Locally on Windows
Conclusion: AI in Your Hands
References

Why Run Local AI Models on Windows 11?

Running AI models locally gives you full control over performance, latency, and the sensitivity of your data. Unlike cloud-based tools that process inputs on remote servers, local deployments handle everything on your own device. This results in quicker response times and full ownership of all data inputs and outputs. For developers and researchers, this setup avoids API limits and Internet-related interruptions.

With the support of Windows Subsystem for Linux (WSL) and tools like Docker and Ollama, people using Windows 11 can build strong offline AI workflows without switching to another operating system. Setting up the environment takes some technical steps, but they are repeatable and achievable with proper guidance.

Also Read: Run Your Own AI Chatbot Locally

Prerequisites and Suggested Hardware

Make sure your system meets the following requirements before installation:

Windows Version: Windows 11 (build 22000 or higher)
RAM: Minimum 16 GB, with 32 GB recommended for LLaMA 3 or similar 7B to 13B models
Storage: 30 to 50 GB of free space for models and support files
GPU (Optional): Nvidia GPU for better performance with CUDA via WSL

Be sure to enable virtualization in your BIOS settings and confirm that Hyper-V is supported and activated. These settings allow WSL to run containers effectively.

Step-by-Step: Install and Setup WSL2

Follow these steps to install WSL2. It provides a Linux-based environment within Windows and is required for running AI models with Ollama.

Step 1: Open PowerShell as Administrator

wsl --install

This command installs WSL2 along with the default Ubuntu distribution. After installation is complete, reboot your machine.

Step 2: Set WSL2 as the default version

wsl --set-default-version 2

Step 3: Launch Ubuntu from the Start Menu

Create a user and set a password. You now have a Linux terminal fully integrated into Windows.

For detailed help, visit our full beginner’s guide to installing WSL on Windows 11.

Install Docker for Windows with WSL Integration

Docker is required for containerized models and works smoothly with Ollama when connected to WSL Ubuntu.

Download and install Docker Desktop for Windows.
During setup, enable WSL integration and select your Ubuntu distro.
Restart your computer to complete the installation process.

Check that Docker is working correctly by running:

docker version

Install Ollama and Run Your First AI Model

Ollama combines large models and inference tools in a user-friendly package. You can download, run, and chat with advanced models using just one command.

Install Ollama in WSL Ubuntu

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

Verify the installation with:

ollama --version

Download and Run LLaMA 3 Model

ollama run llama3

This command downloads the LLaMA 3 model and starts an interactive session. Download time depends on your connection and hardware, but may take up to 15 minutes. After setup, you can chat directly through the terminal.

If you prefer a better interface, start the model using ollama serve and connect with an API or external GUI.

Sample Use via API

Create a Python script to query your local model:

import requests

response = requests.post(
  "http://localhost:11434/api/generate",
  json={"model": "llama3", "prompt": "Explain quantum computing in simple terms"}
)

print(response.json()["response"])

Tips for Performance and Optimization

Use smaller models if RAM is limited: Models such as Mistral or TinyLLaMA work well with less than 8 GB of memory.
Store models on an SSD: Avoid HDDs due to slow data speeds during model loading.
Enable GPU acceleration with CUDA: Install the Nvidia WSL-compatible driver from Nvidia’s website.
Reuse inference data: Store generated tokens locally to reduce repetitive loading.

Alternatives to Ollama

Tool	Platform	Offline Support	Ease of Use
Ollama	Cross-platform	Yes	Beginner-friendly CLI
GPT4All	Windows, macOS, Linux	Yes	Requires manual model import
LM Studio	Windows/macOS GUI	Yes	Best for non-developers

Ollama works well for command-line users and fast testing. LM Studio may appeal more to people who need a simple graphical interface.

Also Read: Machine Learning for Kids: Installing Python

FAQs: Running AI Locally on Windows

Q: Can I run LLaMA or Mistral models without the cloud?
Yes. You can run both LLaMA 2 and LLaMA 3, as well as Mistral, completely offline using Ollama inside WSL on Windows 11.

Q: I only have 8 GB RAM. Is it enough?
Entry-level models such as TinyLLaMA or smaller code-generating models under 3B parameters may run on 8 GB systems, but with reduced speed.

Q: Is Ollama supported on native Windows?
No. Ollama runs under Linux, but functions smoothly inside WSL2 on Windows. Latest features and updates are listed on the Ollama GitHub repository.

Q: Where are Ollama models saved?
By default, models are stored in ~/.ollama/models. Make sure you have at least 30 GB of free disk space for one large model.

Explore additional learning with our guides on What is a Large Language Model? and Best AI tools for local development.

Conclusion: AI in Your Hands

By using WSL2 and Ollama, you can run AI models locally on Windows 11 with stability and performance in mind. This approach gives you speed, privacy, and cost control—all without relying on an Internet connection.