LLM Docker Deployment: Complete Production Guide (2026)

Deploying a Large Language Model (LLM) in a container is a straightforward process that can be completed in about 20 minutes, but maintaining its operation under real-world conditions requires a comprehensive setup involving base image selection, CUDA configuration, and tradeoffs between vLLM, TGI, and Ollama for different use cases. Docker is recommended for its ability to maintain environment consistency, manage dependencies, and ensure data security, making it a preferred choice for self-hosted LLM inference. The guide outlines the setup for single and multi-GPU configurations, emphasizing the importance of compatible CUDA versions and shared memory allocation to prevent container crashes. Production deployment involves a coordinated stack of inference, reverse proxy, and monitoring services, with health checks and metrics monitoring playing crucial roles in maintaining system reliability. For teams aiming to optimize performance and reduce costs, the document discusses implementing strategies like quantization, which can significantly lower GPU memory requirements, and highlights the necessity of fine-tuning models with domain-specific data to enhance performance. The guide also addresses the importance of structured evaluation before deploying new model versions to avoid regressions and suggests using Prem Studio for managing the full AI development lifecycle in a streamlined manner.