If you had a desire to use neural networks to help write code (so-called Vibe coding), and you have a fairly powerful computer, for example with an Nvidia RTX video card, then you can deploy the entire environment absolutely free on your machine. This solves problems with paid subscriptions and allows you to safely work with projects under NDA, since your code is not sent anywhere. In this post I will describe how to assemble a local bundle of LM Studio, VS Code and the Continue extension.
Tools for local Vibe coding
For comfortable work we need three main components:
– LM Studio: a convenient application for downloading and running local LLMs. It takes on all the complexity of working with GGUF models and puts up a local server compatible with the OpenAI API.
– VS Code: a popular and familiar code editor.
– Continue: extension for VS Code that integrates neural networks directly into the work environment. Allows you to chat, highlight code for refactoring, and supports autocomplete.
Hardware requirements
Local language models are memory intensive:
– Video card (GPU): Nvidia with 8 GB VRAM or higher (for comfortable work with models with 7-8 billion parameters). Heavier models will require 16 GB of VRAM.
– Disk space: about 500 GB for storing various downloaded models.
Configuring the link
The setup process is quite simple and does not require complex manipulations in the terminal:
1. Download and install LM Studio. Use the built-in search to find a lightweight model like Qwen Coder or gemma3:12b.
2. In LM Studio, go to the Local Server tab and click Start Server. By default it will start on `http://localhost:1234/v1`.
3. Open VS Code and install the Continue extension from the plugin store.
4. Open the Continue configuration file and add a new model, specifying the `openai` provider and the address of your local server from LM Studio.
You can then communicate with your local LLM directly in the Continue sidebar, ask questions about your code, and generate new components.
Why does this work?
As I wrote earlier, LLMs do better with flat structure and WET (Write Everything Twice) code. Local parameter models may be inferior to giants like GPT-4 when it comes to designing complex architectures, but they are more than capable of generating boilerplate code, refactoring simple functions, and rapid prototyping.
Additionally, with local Vibe coding, your code never leaves the machine. This makes this combination ideal for corporate development and working with sensitive data.
Output
Local neural networks are not capable of fully replacing a programmer or designing a complex system. However, the combination of LM Studio + VS Code + Continue provides independence from cloud services and maintains privacy. This is a completely working auxiliary tool for routine tasks, if you are willing to put up with the limitations of small models and independently control the project architecture.
Links
https://code.visualstudio.com/
https://lmstudio.ai/
https://continue.dev/
Sources
https://youtu.be/IqqCwhG46jY
https://www.youtube.com/watch?v=7AImkA96mE8
