Run Llama 3 Locally with Docker and Ollama for Enhanced Privacy

This guide details how to run the Llama 3 large language model locally on a personal machine using Docker and Ollama. The setup prioritizes privacy by keeping all data on the user's device, eliminating third-party logging and per-token costs. It requires at least 8GB of RAM and 10GB of disk space, with an optional GPU for faster inference. The process involves creating a `docker-compose.yml` file, pulling the Llama 3 model via Ollama within Docker, and then running it for local queries, with a Python client provided for programmatic interaction. AI

IMPACT Enables developers to run LLMs locally, enhancing privacy and control over model parameters and runtime environments.

RANK_REASON Guide on using Docker and Ollama to run an existing LLM locally.

Read on dev.to — LLM tag →

• Apple Silicon
• Docker
• Llama 3
• NVIDIA
• Ollama
• Python
• Windows Subsystem for Linux

• product
• infra

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