How to Run Llama Locally With Docker — From Pull to Private AI Assistant

Pulling Llama with Docker Model Runner uses the Docker CLI with a simple pull command specifying the model name and size. The instructor covers the exact commands, how to verify the download completed correctly, and how to check the model is ready to serve requests during the first module of the workshop.

Both run Llama locally but Docker Model Runner is built into Docker Desktop giving you native Docker integration and shared infrastructure with your other Docker projects. Ollama is a standalone tool. If you use Docker in your development workflow, running Llama with Docker Model Runner is the more integrated approach.

Yes. Docker Model Runner supports running Llama on Apple Silicon including M1, M2, and M3 chips. Apple Silicon actually provides excellent performance for local Llama inference due to the unified memory architecture. The instructor covers Mac-specific configuration during the workshop.

OpenClaw connects to Llama via the OpenAI-compatible API that Docker Model Runner exposes locally. In this workshop you configure the OpenClaw settings to point to your local Docker Model Runner endpoint — allowing OpenClaw to use your locally running Llama model as its AI brain.

Llama 3 8B running locally provides very good quality responses for personal assistant tasks. For complex reasoning and knowledge tasks, larger models (13B and above) provide better quality but require more hardware resources. The instructor covers quality expectations and model selection during the workshop.

Fine-tuning is an advanced topic beyond the scope of this 4-hour workshop. The workshop focuses on running Llama locally with Docker and connecting it to OpenClaw. Fine-tuning resources and approaches are briefly mentioned but the primary focus is building a working private AI assistant.