What if your AI coding assistant never went down, never leaked your code to the cloud, and never charged you a monthly fee? In 2026, that is completely achievable. Running a local, offline AI coding workstation means your code stays private, your tools work without Wi-Fi, and you stay productive even during outages or while traveling. This guide walks you through exactly how to plan, set up, and use a fully offline AI coding workstation — no advanced IT background required.
Requirements / What You Need
Before you start, make sure you have the following hardware and software on hand. The good news is that you do not need a top-of-the-line machine, but there are some minimum thresholds to meet for a smooth experience.
- Computer: A laptop or desktop with at least 16 GB of RAM (32 GB recommended for larger models). A dedicated GPU with 8 GB VRAM or more will dramatically speed things up, but is not mandatory.
- Storage: At least 50 GB of free disk space. AI models range from 4 GB to 30+ GB, so an SSD is strongly recommended for fast load times.
- Operating System: Windows 11, macOS 13+, or a modern Linux distro (Ubuntu 22.04+ works great).
- Software tools: Ollama (local model runner), Visual Studio Code or any code editor, and the Continue extension for VS Code (for AI code suggestions).
- AI model files: Downloaded in advance while you still have internet access. We will cover this in the steps below.
- Optional: A USB drive (32 GB or larger) for portable backups of your model files.
Step 1: Install Ollama on Your Machine
Ollama is the easiest way to run large language models locally on your computer. It handles model downloads, memory management, and exposes a simple local API that other tools can talk to.
- Visit ollama.com while you still have internet access and download the installer for your operating system.
- Run the installer and follow the on-screen prompts. On Windows, you may need to allow it through Windows Defender. On macOS, drag it to your Applications folder as usual.
- Once installed, open a terminal (Command Prompt, PowerShell, or Terminal on Mac/Linux) and type ollama –version to confirm it installed correctly. You should see a version number printed back.
Tip: On Windows, Ollama runs as a background service after installation. You can find its icon in the system tray. Make sure it is running before you try to load any models.
Step 2: Download Your AI Coding Models
This step must be done while you have an internet connection, because the models are large files that get stored locally for offline use afterward.
- Open your terminal and run the following command to pull a solid coding-focused model: ollama pull deepseek-coder-v2. This is a strong open-source model tuned specifically for code generation and explanation.
- If you have limited storage or RAM, try a lighter alternative: ollama pull qwen2.5-coder:7b. This model runs well even on machines with 16 GB of RAM and no GPU.
- Wait for the download to complete. Depending on your connection, this can take anywhere from 5 to 30 minutes. The files are saved locally and do not need to be re-downloaded after this point.
- Test the model by typing: ollama run deepseek-coder-v2 in your terminal. Ask it a simple question like “Write a Python function to reverse a string” to confirm it is working.
Pro Tip: Download at least two models — one larger and more capable, and one smaller and faster. This gives you flexibility depending on your task and system load.
Step 3: Set Up Visual Studio Code with the Continue Extension
Visual Studio Code (VS Code) is free, lightweight, and works entirely offline after setup. The Continue extension connects VS Code directly to your local Ollama models, giving you inline AI code suggestions, chat, and explanations — all without sending anything to the internet.
- Download and install VS Code from code.visualstudio.com while online.
- Open VS Code, go to the Extensions panel (the square icon on the left sidebar or press Ctrl+Shift+X), and search for Continue. Install the extension by Continue Dev.
- After installation, click the Continue icon in the sidebar. It will ask you to configure a model. Choose Ollama as the provider and select the model you downloaded in Step 2.
- Set the API base URL to http://localhost:11434 — this is where Ollama listens locally on your machine.
- Save the configuration and test it by opening any code file, highlighting a function, and pressing Ctrl+I to open the inline AI prompt. Type “explain this code” and see the response appear in the sidebar.
Step 4: Organize Your Offline Project Workspace
A good offline workstation is not just about the AI tool — it is also about having everything you need ready to go before you disconnect.
- Create a dedicated project folder on your desktop or documents directory. Name it something clear like OfflineProjects.
- Install any language runtimes you need — Python, Node.js, Java, etc. — while you are online. These do not require internet to run afterward.
- Download offline documentation. Python’s official docs, MDN Web Docs, and many frameworks offer downloadable offline versions (often as ZIP or .devhelp files). Store these in your project folder.
- Set up a local Git repository using git init inside your project folder. This lets you track changes, roll back mistakes, and stay organized — all without needing GitHub access.
Tip: Install the Zeal documentation browser (zealdocs.org) before going offline. It contains downloadable documentation for over 200 programming languages and frameworks in one searchable app.
Step 5: Test Your Full Offline Setup
Before you rely on this setup in a real work situation, do a dry run in airplane mode to catch any issues early.
- Disconnect from the internet completely — switch to airplane mode or unplug your ethernet cable.
- Open VS Code and confirm the Continue extension still connects to your local Ollama instance. Ask it to generate a sample function.
- Open your offline documentation browser and search for a topic you commonly reference.
- Run a small script using your locally installed language runtime to confirm everything executes correctly.
- Commit a small change using Git to confirm version control works offline.
If all five of those steps work without errors, your offline AI coding workstation is fully operational.
Troubleshooting Tips
Ollama is not responding or the API is unreachable
Make sure the Ollama service is actually running. On Windows, check the system tray. On macOS and Linux, run ollama serve in a terminal to start it manually. Then try reloading VS Code and the Continue extension.
The AI model responds very slowly
This usually means your system does not have enough RAM or is using CPU-only processing. Try switching to a smaller model like qwen2.5-coder:7b which uses less memory. Also close any background apps (browsers, video players) to free up RAM.
Continue extension shows “model not found” error
Double-check the model name in the Continue configuration matches exactly what you downloaded. Run ollama list in your terminal to see all available models and copy the exact name from that list into the Continue settings.
VS Code extensions are asking to update or connect online
This is normal. Simply dismiss those prompts. All core functionality — editing, the AI assistant, Git — works fully offline. Updates can wait until you are back online.
Not enough disk space for model files
Use a USB 3.0 drive or an external SSD to store the model files. Ollama supports custom model storage paths. On most systems you can set the OLLAMA_MODELS environment variable to point to an external drive location.
Wrapping Up
Building an offline AI coding workstation in 2026 is more accessible than most people expect. With Ollama handling the local model runtime, VS Code and Continue providing a polished AI-assisted coding environment, and a few smart preparation steps, you get a private, fast, and reliable setup that works anywhere — no subscription, no Wi-Fi required, no data sent to any server.
Start with a small coding model, test everything in airplane mode before you need it for real, and gradually add offline documentation and Git workflows to round out your environment. Whether you are a student coding on a campus with spotty Wi-Fi, a freelancer working in a café, or a small-business owner who prioritizes data privacy, this setup puts powerful AI tools entirely in your hands.
Frequently Asked Questions
Do I need a GPU to run local AI models?
No, but it helps a lot. Without a GPU, models run on your CPU and respond more slowly — usually 5 to 30 seconds per response depending on the model size. For everyday coding help, this is often perfectly acceptable. With a GPU, responses are nearly instant.
Are these local AI models as good as ChatGPT or GitHub Copilot?
For code-specific tasks in 2026, models like DeepSeek Coder V2 and Qwen 2.5 Coder are surprisingly competitive. They handle explanations, debugging help, and code generation well. They may lag behind the largest cloud models on very complex reasoning, but for most everyday coding tasks, the gap is small.
Can I use this setup on a Chromebook?
Standard Chromebooks are not well-suited for this because Ollama requires a full Linux or Windows environment. However, if your Chromebook supports Linux (via Crostini) and has enough RAM, it is technically possible — though performance will be limited.