The promise of running powerful LLMs like Llama 2 or Mistral locally is compelling, offering unparalleled data privacy and cost control. However, the reality often involves navigating a maze of environment setup, CUDA drivers, and platform-specific quirks that can derail development.

In this session, we present a solution: the Docker Model Runner. We will explore how containerization solves the core challenges of local LLM deployment by providing isolated, reproducible, and portable environments. You will learn how to use this tool to pull, configure, and run state-of-the-art models with a simple CLI, abstracting away the underlying complexity.

We will cover practical use cases, from rapid prototyping and development to building scalable, containerised inference services. Attendees will leave with a clear, practical framework for integrating private LLMs into their applications, finally making local deployment as accessible as it is powerful.