How to Run a Local AI Coding Assistant in VS Code
How to Run a Local AI Coding Assistant in VS Code
A local AI coding assistant lets you ask programming questions, explain code, and generate or modify snippets using a language model running on your computer. One practical setup combines Ollama with Visual Studio Code (VS Code). It can reduce dependence on cloud-based inference, although performance depends on your hardware and selected model.
What Do You Need to Run Local AI in VS Code?
You need three components:
VS Code: Your code editor.
Ollama: Software that downloads and runs compatible language models locally.
A coding model: The AI model responsible for generating and explaining code.
You also need sufficient storage, memory, and processing power. Smaller, quantized models generally require fewer resources, while larger models can offer stronger capabilities but demand more memory and may respond more slowly.
A dedicated GPU can accelerate supported workloads, but a powerful GPU is not mandatory for every local model. CPU-only inference is possible, although speed may be limited. Check the requirements of your chosen model before downloading it.
Step 1: Install Ollama and Download a Model
Download Ollama from its official website and install the version appropriate for your operating system.
Open a terminal and download a compatible model using the command shown in its official documentation. For example, the Ollama VS Code extension documentation currently demonstrates:
ollama pull qwen3.6
This downloads the named model to your local Ollama installation. Model availability and resource requirements can change, so check the model's current documentation before selecting it.
After the download finishes, run:
ollama list
This command lists the models available locally. Confirm that your chosen model appears before proceeding.
Step 2: Connect Ollama to VS Code
Install the official Ollama extension for VS Code.
Then follow these steps:
Start Ollama and ensure it is running.
Open VS Code and select Chat.
Open the model picker at the bottom of the chat input.
Select your model from the Ollama section.
Ask a programming question to test the connection.
The extension discovers models from the local Ollama server at http://127.0.0.1:11434 by default. Follow the official extension instructions if you use a different server configuration.
For example, ask the assistant to explain a Python function, identify a bug, or write a basic unit test. Review its response and test any suggested code before incorporating it into your project.
Step 3: Choose a Model That Fits Your Hardware
The best local coding model is not necessarily the largest one. Choose according to your workload, available memory, response speed, and programming-language needs.
Learning and simple scripts: Start with a smaller coding-capable model.
Debugging and code explanations: Test how accurately it understands your files and error messages.
Larger projects: Consider models with suitable context capacity and enough memory to handle the code you provide.
RAM, VRAM, model quantization, and context length all affect performance. A model that fits in memory may still respond slowly, and a model's advertised context capacity does not guarantee that your computer can run it efficiently.
Can You Use a Local AI Coding Assistant Offline?
Yes, once the necessary software and model files are downloaded, local inference can work without an internet connection. However, initial downloads require connectivity, and some extensions or editor features may still depend on online services.
Local processing can reduce the amount of source code sent to external model providers. Nevertheless, review extension permissions, telemetry settings, connected tools, and network access before using confidential business code.
Also remember that local models can generate incorrect code, insecure suggestions, or outdated instructions. Run tests, inspect dependencies, and review security-sensitive changes yourself.
Troubleshooting Common Problems
If your model does not appear in VS Code, check that Ollama is running and execute ollama list. Next, run Ollama: Refresh Models from the VS Code Command Palette. If the problem persists, use Ollama: Diagnose Models and review the Ollama output channel.
If responses are too slow, try a smaller model, reduce unnecessary context, close memory-intensive applications, or check whether supported GPU acceleration is working. Avoid exposing the Ollama service to untrusted networks when troubleshooting connectivity.
Bottom line: Ollama and VS Code provide a practical starting point for experimenting with local AI-assisted programming. Begin with a model your computer can handle, test it on a small project, and expand only when its accuracy, speed, and resource usage meet your needs.