The push for data privacy and a desire for greater control over AI workflows are driving a growing interest in running large language models (LLMs) locally. While cloud-based services like GitHub Copilot have become staples for many developers, a new approach allows programmers to leverage the power of LLMs directly within their Integrated Development Environment (IDE) – specifically Visual Studio Code – without transmitting their code to external servers. This is becoming increasingly achievable through tools like Ollama and extensions such as Continue.
Running an LLM locally offers several key advantages. Perhaps most importantly, it addresses privacy concerns by keeping code on the developer’s machine, avoiding the sharing of potentially sensitive data. Speed can also improve, as local processing eliminates the latency inherent in cloud-based services. Offline functionality is another benefit, ensuring uninterrupted coding even without an internet connection. Finally, local LLMs offer a degree of customization, allowing developers to experiment with different models and tailor their AI-assisted coding experience.
Ollama simplifies the process of downloading and running various open-source LLMs. It currently supports macOS, Linux, and Windows (via the Windows Subsystem for Linux, or WSL). Once Ollama is installed, developers can pull and run models with a simple command. For example, the LLaMA 3 model can be initiated with the command `ollama run llama3`.
The integration with Visual Studio Code is facilitated by extensions like Continue. According to documentation, users can access the Copilot sidebar within VS Code, manage models through a dropdown menu, and select Ollama as the provider to access locally running models like qwen3 or qwen3-coder:480b-cloud. This allows developers to receive AI-powered code suggestions directly within their familiar coding environment.
The rise of LLMs has fundamentally changed software development, offering assistance with code generation and completion. However, the reliance on cloud services introduces potential drawbacks. Local LLMs represent a shift towards greater developer control and security. This approach is gaining traction as developers seek more autonomy over their workflows and data.
The benefits extend beyond just privacy and speed. A local LLM also avoids the potential costs associated with cloud-based services, which can be significant, particularly for intensive use. The ability to customize and experiment with different models allows developers to fine-tune the AI assistance to their specific needs and coding style. This level of control is not always available with cloud-based solutions.
Installing Ollama is the first step in this process. The Ollama website provides installation instructions for each supported operating system. Once installed, the Continue extension within VS Code provides the necessary bridge to access and utilize the locally running LLM. The combination of these tools creates a powerful and private coding environment.
The ability to run LLMs locally isn’t just about replicating the functionality of cloud-based services; it’s about offering a different set of trade-offs. While cloud services benefit from massive scale and constant updates, local LLMs prioritize privacy, control, and offline access. This makes them particularly appealing to developers working with sensitive data or in environments with limited internet connectivity.
As the open-source LLM landscape continues to evolve, tools like Ollama will play an increasingly important role in democratizing access to this powerful technology. By simplifying the process of running and integrating these models, Ollama empowers developers to harness the benefits of AI without compromising their privacy or control.
