Local large language models are gaining traction as developers seek privacy, offline access, and reduced costs. This guide walks through deploying Llama 3.1 8B using Ollama, a lightweight tool for running models locally, and integrating it with VSCode via the Continue extension for AI-assisted coding. The setup is straightforward: install Ollama, pull the Llama 3.1 model, configure Continue in VSCode, and start using the local LLM for code completion, explanation, and debugging. This approach eliminates API costs and data privacy concerns, making it ideal for indie hackers and small teams. The guide also covers troubleshooting common issues like GPU memory limits and model performance tuning. As local models improve, such workflows become increasingly viable for production use.
This post details how to deploy Llama 3.1 8B locally using Ollama and integrate it with VSCode as a code assistant via the Continue extension. It matters because local LLMs offer privacy, offline capability, and cost control for developers and small teams.