Due to the emergence of AI coding assistants, a growing number of developers are in search of solutions that focus on privacy, speed, and flexibility. Running an LLM locally with Visual Studio Code (VS Code) can provide all three: eliminate dependencies on the cloud and control exactly what it is you develop on. This is a how-to setup the local LLM in VS Code.
It Is Better to wing local?
These are the three main benefits to running a LLM on your personal machine:
Speed & Reliability
Local models remove network latencies and constraints such as APIs. It responds with speed to even complicated prompts.
Data Privacy
Your code and prompts are not sent out of your device. This is essential to projects dealing with succeptible or commercial code.
Customization & Control
You have the flexibility to customize models, system prompts, and tool behavior specific to your workflow – without overreaching vendor imposed restrictions.
You will need
A powerful machine (multi-core or a GPU-enabled)
Any local LLM provider (i.e., Ollama or llama.cpp).
The continued VS Code add-fbavtrAddoma
Step-by-Step Setup
To install a local LLM framework see https://
Demos such as Ollama or llama.cpp allow you to download and execute open-source models on your computer.
- Install Ollama or llama.cpp on your OS.
Use the tool to fetch a model (e.g. CodeLlama 7B or DeepSeek-R1).
And just run your model as a local server (usema simple command like:
ollama serve llama-model:tag
- Install the Continue Extension on VS Code
Open VS Code, visit the Extensions tab and search and install Continue. This offers chat and autocomplete functions enabled by a local LLM.
- Configure the Model in Continue
Edit the config.json for the extension—typically in your home directory—to point Continue to your local server:
{
“models”: [
{
“title”: “Local LLM”,
“provider”: “ollama”,
“model”: “llama-model:tag”,
“apiBase”: “http://localhost:11434”,
“systemMessage”: “Act as my coding assistant.”
}
]
}
- Use Chat and Autocomplete Locally
Open the Continue pane, select the configured model, and you’re ready. Ask it to explain code, write snippets, or refactor all with instant local responses.
Processing of limited hardware
If your hardware is not powerful enough to run a large model, then you can consider a hybrid set up:
cache light models on-the-fly on local things.
Pull down the heavier workload via cloud APIs–set as alternative to your local LLM.
Advantages and disadvantages of local LLM integration
Pros:
Ultra-low response times
Code privacy full
full AI behaviour control
Cons:
Needs high-System
The complexity may be increased withitledrawhacc proudendSeptember 1st, 2018
Smaller models can be inferior in performance of cloud giants
Final Thoughts:
Having a local LLM integrated into VS Code gives developers access to a powerful AI assistant that is fast, privacy-aware, and integrates into your specific workflow. It is a very strong option instead of cloud-only tools and the new direction in the approach of AI-first development practices.
