How to Run an LLM in Docker: A Complete Developer’s Guide

Large Language Models (LLMs) such as Llama, Mistral, and Falcon have become integral to modern software systems. From powering chatbots to enhancing enterprise automation, they bring advanced natural language capabilities into web and backend environments.
However, deploying these models can be challenging due to complex dependencies, environment conflicts, and hardware requirements.

Docker offers a clean, consistent solution. It enables you to package an LLM and all its dependencies into a portable container that runs identically on any system, whether local, in CI/CD, or in the cloud.

This guide walks through the process of running an LLM inside Docker, setting up your environment, creating an image, and exposing your model via an API.

Why Run LLMs in Docker

LLMs often require specific Python versions, library builds, and GPU configurations. Reproducing these environments across systems can be error-prone and time-consuming. Docker eliminates that issue by encapsulating your entire runtime in a lightweight, isolated container.

Key advantages include:

Consistency: Your model runs the same way on every machine.

Portability: Deploy to any environment that supports Docker.

Scalability: Easily spin up multiple instances or integrate with orchestration tools like Kubernetes.

Isolation: Avoid dependency conflicts with other projects.

In practice, containerizing an LLM simplifies everything from testing to production deployment, especially for teams building APIs or web-integrated AI systems.

Prerequisites

Before starting, ensure your development environment includes the following.

Tools:

• Docker and optionally Docker Compose
• Python 3.x
• Git

Hardware:

A GPU is optional, but strongly recommended for faster inference when working with larger models.

Knowledge:

• Basic Python and command-line experience
• Familiarity with REST API concepts
• Lightweight Open-Source LLMs for Testing:
• llama.cpp– CPU-friendly Llama implementation
• Ollama– Simplified local LLM runner
• Falcon– Open-weight transformer model

Setting Up the Project Environment

To begin, set up a local environment and ensure the model runs correctly before containerizing it.

Verify that the LLM executes locally by running a sample inference command or script. Once confirmed, you’re ready to move the setup into Docker.

Writing the Dockerfile

Create a file named Dockerfile in your project root directory with the following content:

FROM python:3.10-slim: Uses a minimal Python base image to keep the container lightweight.

WORKDIR /app: Defines the working directory inside the container.

Explanation

FROM python:3.10-slim: Uses a minimal Python base image to keep the container lightweight.

WORKDIR /app: Defines the working directory inside the container.

COPY requirements.txt . and RUN pip install: Installs dependencies before copying the rest of the files to leverage Docker’s build cache.

COPY . .: Adds the application files.

CMD ["python", "app.py"]: Runs the application when the container starts.

For complex projects, consider multi-stage builds to minimize image size.

Optimization Tips:

Use a .dockerignore file to exclude unnecessary files:

For complex projects, consider multi-stage builds to minimize image size.

Building and Running the Container

To build and launch your Docker image:

Build the image:

Run the container:

If your application exposes an API, access it via http://localhost:8000.
If it’s CLI-based, you can enter the container shell:

Exposing the LLM as an API

To make your LLM accessible over HTTP, you can wrap it with a lightweight FastAPI server.

Rebuild and run your container:

Access the API endpoint:

You should receive a JSON response, such as:

You’ve now successfully containerized and exposed your model as a REST API.

Using Docker Compose or GPU Support

Docker Compose

If you want to manage multiple containers (for example, an LLM API and a frontend), use Docker Compose.

docker-compose.yml example:

Run it with:

GPU Support

If your system includes an NVIDIA GPU, you can enable it with:

Ensure the NVIDIA Container Toolkit is installed for GPU passthrough.

Final Thoughts and Next Steps

Running an LLMhttps://www.cloudflare.com/learning/ai/what-is-large-language-model/ inside Docker provides a scalable and maintainable approach to deploying AI models. You now have a repeatable process for:

• Packaging an LLM and its dependencies
• Running it in a controlled environment
• Exposing it via an API for web or enterprise integration
• Extending the setup to handle multiple services or GPUs
• For production, consider deploying your Dockerized model using:
• AWS ECS or Fargate for managed container hosting
• Azure Container Apps or Google Cloud Run
• Kubernetes for large-scale orchestration

Containerizing AI workloads bridges the gap between research and production by ensuring consistency, scalability, and ease of deployment. Once your LLM works within Docker, it becomes deployable anywhere your application stack needs it.