How to Run a Self-Hosted LLM in 2026 — From Setup to Production

Self-hosting an LLM in 2026 means running a large language model on hardware you own or control — your laptop, desktop, or a VPS — using tools like Docker Model Runner. Your model runs locally, processes all requests on your own hardware, and sends no data to external AI providers. In this workshop you self-host an LLM and connect it to OpenClaw to build a complete private AI assistant.

The best self-hosted LLMs in 2026 for most developer setups are Llama 3 8B, Mistral 7B Instruct, and Phi-3 Mini. All are available through Docker Model Runner, all are free, and all deliver strong performance for personal AI assistant use cases. The instructor covers the trade-offs between each during the workshop.

Self-hosted LLMs require significant disk space — typically 4GB to 8GB per model for quantised versions of 7B to 8B parameter models. The instructor covers storage requirements for different models and how to manage model storage efficiently during the workshop.

You can have multiple LLMs installed through Docker Model Runner but running them simultaneously requires sufficient RAM for each. The workshop covers how to manage multiple models efficiently and how to configure OpenClaw to switch between them for different use cases.

Self-hosting an LLM is more accessible in 2026 than it has ever been, but it still requires comfort with Docker, command-line tools, and basic system administration. This workshop is designed for developers — not beginners. If you are comfortable with Python and basic terminal usage, you have the foundation needed to follow this workshop successfully.

Keeping your self-hosted LLM current in 2026 involves pulling updated model versions through Docker Model Runner when they are released. New versions of popular models are released regularly. The instructor covers the update process and how to evaluate whether a new model version is worth upgrading to during the workshop.