AI is changing the way we write and understand code, and one of the recent developments is the use of local AI code assistants. Unlike cloud-based solutions, these assistants run directly on your machine, offering more privacy, speed, and flexibility. Best of all, they are free.
Although open-source models may not match the capabilities of some commercial models, they continue to improve rapidly and often meet the needs of many developers.
Let’s have a look what you need to know about them, why they’re worth considering, and how to set one up.
What is a Local AI code assistant?
A local AI code assistant is an AI-powered tool designed to help you write, debug, and optimize code, integrated into your coding environment (Alvi, 2024).
Instead of relying on remote servers, local AI assistants use large language models (LLMs) installed on your computer. These models are used in coding tasks, such as auto-completion, code analysis, and document generation.
Local assistants work within popular coding environments like Visual Studio Code (VS Code) and can improve your workflow without the need for separate applications.
VS Code extensions, such as Cline, does the communication between the AI model and your IDE (Cline, n.d.).
Why run a code assistant locally?
Using local AI assistant has several benefits over cloud-hosted solutions.
Cloud-hosted solutions usually have subscription fees, but local solutions are usually free as there is many free open-source models and platforms to use.
They are also more private. When the AI runs on your machine, sensitive code and data stays local. No need to worry about data leaks or compliance issues.
Local solutions can also offer faster response times as there is no need for network requests. Models time to response (inference) depends on the hardware it is being run on (Benefits of Local Large Language Models, 2024)
What do you need to get started?
Setting up a local AI code assistant does require some initial preparation.
You’ll need capable hardware. Running LLMs locally demands decent processing power. A GPU with decent amount of VRAM is often recommended for optimal performance. CPUs can work, but they are slower (Allman, 2024).
You’ll also need software. Tools like Ollama or similar platforms provide the infrastructure to run local LLMs. These software packages come with pre-configured environments to simplify setup. Ollama works on Windows, Linux and macOS (Ollama, n.d.).
Ollama has a long list of models to choose from. It’s recommended to choose a model that matches your hardware specifications and are optimized for coding tasks. Many models, such as Qwen 2.5 or LLama 3.2, has several sizes of models available. Smaller sized models require less from hardware but also provides less coding capabilities compared to larger models. If you are going to use the model for commercial purposes, remember to check the model’s license.
To get Ollama and chosen model to work with your coding environment, you need to install extensions for your preferred IDE. Some IDEs, such as Cursor (not free) comes with built-in AI integrations. For others, for example VS Code, plugins like Cline are useful for AI assistant to work within your editor.
If you haven’t tried one yet, now is the perfect time to explore this new way of working and see how it can change the way you code.
References
- Allman, J. (2024). Tech Primer: What hardware do you need to run a local LLM? Puget Systems. https://www.pugetsystems.com/labs/articles/tech-primer-what-hardware-do-you-need-to-run-a-local-llm/
- Alvi, F. (2024). AI Coding Assistants in 2024: Choosing the Right Tool for Your Development Needs. OpenCV. https://opencv.org/blog/ai-coding-assistants/
- Benefits of Local Large Language Models. (2024). SideEffekt. https://sideeffekt.com/2024/07/20/benefits-of-local-large-language-models/
- Cline. (n.d.). Cline (prev. Claude Dev) – #1 on OpenRouter. Github. https://github.com/cline/cline
- Ollama. (n.d.). Ollama. Github. https://github.com/ollama/ollama
