LLM Docker Deployment: Complete Production Guide (2026)

Deploying a Large Language Model (LLM) in a container involves a multi-step process that ensures it runs efficiently under real traffic and can handle restarts while providing monitoring capabilities. The guide emphasizes the importance of using Docker for LLM deployment to maintain consistent environments and avoid common issues like CUDA toolkit mismatches and Python dependency conflicts. It outlines the setup of CUDA and base images, comparing deployment options like vLLM and TGI based on throughput, memory efficiency, and observability. Key steps include configuring single and multi-GPU deployments, setting up a production Docker Compose stack with health checks and monitoring, and managing secrets securely. The document highlights the significance of multi-stage Docker builds to keep final images lean, using non-root users for security, and managing model updates without downtime through load balancers. Additionally, it stresses the importance of fine-tuning models on domain-specific data to improve performance and suggests strategies for reducing costs in self-hosted setups.

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