How to Containerize Your Local LLM

This guide outlines the process of containerizing a local Large Language Model (LLM), such as LLaMA2, to create a scalable and portable API service. By decoupling model storage from the container, it facilitates efficient deployment and integration into applications. Medium

Key Insights

• Microservice Architecture: Encapsulates LLM logic within a container, promoting scalability and maintainability.
• External Model Storage: Stores large model files outside the container to reduce image size and simplify updates.
• Practical Implementation: Provides step-by-step instructions using Intel’s open-source resources.
• Modular Design: Supports integration with various front-end interfaces and deployment environments.