How To Run Ollama Docker: Complete 2025 Setup Guide

Running large language models (LLM’s) in 2025 requires very efficient and developer-friendly tools, like the amazing Ollama Docker. Most AI tools just rely on cloud-based API’s or heavy system setups, but combining Ollama with the simplicity of Docker and the flexibility of local model execution, giving developers full control, privacy, and probability.

In this guide, I’m telling you HOW to get that efficiency for your LLM’s. Together, we explore what Ollama Docker is and the best ways to install and set up the tool in 2025.

What Is Ollama Docker? (Quick Overview)

Using Ollama in Docker enables you to package AI workflows, ensuring your model, environment, and dependencies remain uniform across different machines and teams.

Whether you are creating AI applications, testing models offline, or setting up lightweight inference servers, Docker guarantees a tidy, repeatable configuration each time.

How to Install Ollama with Docker (Step-by-Step)

Prerequisites

Ensure you have the following prerequisites installed on your machine:

Docker (must also be able to run docker compose …)

GPU Support (Optional)

If you possess a GPU and wish to use its capabilities within a Docker container, follow these steps to install the NVIDIA Container Toolkit:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \<br>&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \<br>sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \<br>sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list<br>sudo apt-get update<br>sudo apt-get install -y nvidia-container-toolkit

Configure NVIDIA Container Toolkit

sudo nvidia-ctk runtime configure --runtime=docker<br>sudo systemctl restart docker

Test GPU integration

docker run --gpus all nvidia/cuda:11.5.2-base-ubuntu20.04 nvidia-smi

Configuration

Clone the Docker Compose repository:

git clone https://github.com/mythrantic/ollama-docker.git

Change to the project directory:

cd ollama-docker

Usage

Start Ollama and its dependencies using Docker Compose:

if gpu is configured

docker compose -f docker-compose-ollama-gpu.yaml up -d

else

docker compose up -d

Best Practices for Using Ollama with Docker

• Ollama is a free tool that allows users to run, create, and share large language models (LLMs) on their own devices.
• It works with different models like Llama 3, Mistral, and Gemma, offering an easy-to-use command-line interface.
• Ollama is compatible with operating systems such as macOS, Linux, and Windows.
• To simplify installation and management, it can also be deployed in a Docker container.

1. Always use the official Ollama Docker image.

Make sure to pull the official image from Docker Hub (ollama/ollama) for compatibility and security.

docker pull ollama/ollama

Avoid using third-party builds that may have outdated versions or unverified dependencies.

2. Run Containers with Limited Privileges

To enhance security, do not run Ollama containers as root. Use the –user flag to set a non-root user:

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docker run -d --user 1000:1000 ollama/ollama

This minimizes risks if there are any vulnerabilities in the container.

3. Persist Model and Config Data with Volumes

Use persistent volumes to prevent losing downloaded models or configurations when the container restarts:

docker run -d -v ollama_data:/root/.ollama ollama/ollama

This enables quick restarts without needing to re-download model files.

4. Enable GPU Acceleration (If Available)

If your system has NVIDIA GPUs, allow Docker to access the GPU for quicker model inference:

docker run --gpus all -d ollama/ollama

This significantly speeds up model response times for larger LLMs.

5. Use Docker Compose for Multi-Service Setups

When connecting Ollama with APIs, databases, or web services, set everything up in a docker-compose.yml for simpler orchestration:

services:<br>ollama:<br>image: ollama/ollama<br>volumes:<br>- ollama_data:/root/.ollama<br>ports:<br>- "11434:11434"<br>volumes:<br>ollama_data:

6. Keep Your Docker and Ollama Images Updated

Regularly pull the latest versions to benefit from performance enhancements, model updates, and security fixes:

docker pull ollama/ollama:latest

Also, update Docker itself to ensure it works well with new Ollama releases.

7. Monitor Resource Usage

Ollama can use a lot of resources. Use Docker stats to check CPU and memory usage:

docker stats ollama

Adjust container limits (–memory, –cpus) to keep performance stable.

8. Clean Up Unused Images and Containers

Regularly delete old containers and images to free up disk space:

docker system prune -a

This helps maintain your environment.

Summary!

Using Ollama Docker in a Docker setup provides a flexible and effective way to use LLMs. Docker simplifies installation and creates separate environments, enabling anyone, whether an experienced developer or a beginner, to effectively harness AI’s power.

FAQs