Local LLMs Replace Cloud Coding Assistants for Offline Work
Developers are swapping cloud-based AI for local models to keep coding without internet access.
Key points
- The setup uses Qwen 2.5 Coder models via LM Studio and the Continue extension.
- Local models enable coding features like autocomplete and chat without internet.
- Performance is lower than cloud AI, but offline access is a major benefit.
A developer replaced GitHub Copilot with local AI models to work offline. This change allows coding without an internet connection.
The setup uses two specific models and one extension. It runs entirely on the user's computer, avoiding cloud dependencies.
Replacing Cloud AI With Local Models
The user runs two models through LM Studio. These are Qwen 2.5 Coder 1.5B and Qwen 2.5 Coder 7B. LM Studio manages the models but does not connect to VS Code directly.
An extension called Continue bridges the gap. It connects VS Code to the local models. This setup replaces the need for cloud-based Copilot.
Choosing Models For Speed And Power
The smaller 1.5B model handles autocomplete. It is faster and uses fewer resources. The larger 7B model handles chat and code editing.
The 7B model is more capable but demanding. Users must configure Continue to use the right model for each task. This prevents conflicts between different completion providers.
Trade-Offs Between Performance And Privacy
Local models cannot match cloud-based speed. A 7-billion-parameter model is less autonomous than large cloud agents. However, it works completely offline.
This setup is ideal for travelers. It allows coding without internet access. XDA Developers notes this is a viable alternative for those prioritizing privacy and offline capability.
