The rise of large language models (LLMs) has dramatically altered the landscape of software development, offering tools like GitHub Copilot to assist programmers with code generation and completion. However, concerns around data privacy and reliance on cloud-based services have spurred interest in running these powerful AI models locally. Now, developers can leverage the power of LLMs directly within their Integrated Development Environment (IDE) – specifically Visual Studio Code – without sending their code to external servers. This is achievable through a combination of Ollama, a tool designed to simplify running open-source LLMs, and extensions like Continue, which integrate these models into VS Code.
Running an LLM locally offers several key advantages. Privacy is paramount, as your code remains on your machine, avoiding the sharing of sensitive data with third parties. Speed can also improve, as local processing eliminates the latency associated with cloud-based services. Local LLMs provide offline functionality, ensuring uninterrupted coding even without an internet connection. Finally, the ability to customize and experiment with different models offers developers greater control over their AI-assisted coding experience. This approach is gaining traction as developers seek more control and security over their workflows.
Installing Ollama and Integrating with VS Code
The first step towards local LLM code completion is installing Ollama. Ollama supports macOS, Linux, and Windows (via WSL – Windows Subsystem for Linux) and simplifies the process of downloading and running various open-source LLMs. Installation instructions are readily available on the Ollama website. Once installed, you can pull and run models like LLaMA 3 with a simple command: ollama run llama3. This will initiate an interactive chat session in your terminal, allowing you to test the model’s capabilities.
To bring this functionality into VS Code, the Continue extension is essential. This extension acts as a bridge between VS Code and Ollama, enabling seamless integration. After installing the Continue extension from the VS Code marketplace, you can configure it to connect to your local Ollama instance. Within the Continue settings, select “Ollama” as the provider and choose the desired model, such as “qwen3” or “qwen3-coder:480b-cloud” as outlined in the Ollama documentation.
Benefits of Local LLM Development
The advantages of running LLMs locally extend beyond privacy and speed. According to Keyhole Software, local LLMs offer a greater degree of customization, allowing developers to experiment with different models to find the optimal solution for their specific needs. This level of control is particularly valuable for teams working on specialized projects or with unique requirements. Avoiding the costs associated with cloud-based services can lead to significant savings, especially for organizations with large development teams.
The process of setting up a local LLM with Ollama and VS Code is surprisingly straightforward, as noted by one developer on Medium. This accessibility is democratizing access to powerful AI tools, empowering developers to enhance their productivity and creativity without compromising data security or incurring substantial costs.
Future Developments and Considerations
The integration of local LLMs into development workflows is still evolving. Ongoing development efforts are focused on improving model performance, expanding model compatibility, and streamlining the integration process. As the ecosystem matures, we can expect to spot even more sophisticated tools and extensions emerge, further enhancing the capabilities of local LLM-powered code completion. The trend towards local AI development is likely to accelerate as developers prioritize privacy, control, and cost-effectiveness.
Have you experimented with running LLMs locally? Share your experiences and thoughts in the comments below. Don’t forget to share this article with your fellow developers!
