How to Deploy a Custom LLM in the Cloud Using Docker

Deploying a custom Large Language Model (LLM) in the cloud has been made more accessible with the use of containerization technologies like Docker and cloud GPU providers such as Runpod. The process involves building a Docker container with the LLM, tokenizer, and an inference server, configuring GPU runtime for efficient inference, and exposing the model through HTTP endpoints. The guide details using Hugging Face's text-generation-inference server, preparing model files, writing a Dockerfile, and pushing the Docker image to a container registry for deployment on Runpod. It also covers selecting appropriate GPUs and scaling the deployment across multiple pods for production use, with an emphasis on cost-effective cloud computing. The guide highlights the ease of automating deployments with Runpod's API and provides resources for further assistance, encouraging users to leverage prebuilt LLM templates to expedite the deployment process.