Last updated July 2026.
Deploying Open WebUI and Ollama via Docker Compose creates a fully private, offline ChatGPT alternative. By running Ollama for local LLM inference and Open WebUI for a modern web interface, you gain complete control over your data with no per-token costs. The stack supports user authentication, model downloading directly from the UI, and integrations with pipelines for retrieval-augmented generation (RAG).
Public cloud AI interfaces are incredibly convenient, but they require sending every keystroke to third-party servers. For developers working with sensitive codebases, proprietary APIs, or personal documentation, this data transmission is a dealbreaker. By deploying Open WebUI and Ollama on your own hardware, you can establish an enterprise-grade, local-first conversational interface. This setup provides a seamless ChatGPT-like experience powered entirely by open-weights models like Qwen 2.5, Llama 3, or Mistral, running locally on your hardware.
The Docker Compose Setup
To keep the installation clean and easily reproducible, we orchestrate the entire stack using Docker Compose. This allows us to define both the Ollama backend (configured to leverage Nvidia GPU acceleration if available) and the Open WebUI frontend in a single file. By using a shared Docker network, the web interface can securely communicate with Ollama’s API endpoint without exposing Ollama directly to the host network.
Handling GPU Acceleration
If you are running the stack on a system with an Nvidia GPU, ensuring Docker can access the GPU’s CUDA cores is essential for acceptable tokens-per-second performance. You must install the Nvidia Container Toolkit on your host OS and include the resources reservations block in your docker-compose.yml file. Without this configuration, Ollama will fall back to CPU execution, which is significantly slower for models larger than 8B parameters.
For systems running on CPU only (such as homelab servers or mini PCs), you can omit the Nvidia deploy block. Modern multi-threaded CPU inference via Ollama is surprisingly viable for smaller 3B or 1.5B models, making it perfect for low-power background assistants.
User Authentication and Multi-User Management
Unlike standard local setups that assume a single user, Open WebUI includes built-in admin controls and user management. When you first access the interface, the first account registered automatically becomes the Administrator. From the admin panel, you can disable public registration, approve new users manually, and assign specific model access rules. This makes it safe to share your self-hosted instance with family members or team members without exposing admin configurations.
Additionally, Open WebUI supports web search integration and document ingestion out of the box, allowing users to upload PDFs or text files to run local RAG queries directly from the chat window.
Frequently Asked Questions
Q: Does Open WebUI support OpenAI and Anthropic APIs alongside Ollama?
A: Yes. You can configure Open WebUI to connect to external endpoints like OpenAI, Anthropic, or OpenRouter by adding the corresponding API keys and base URLs in the admin settings panel.
Q: How do I update the LLM models used by Ollama?
A: You can pull new models or update existing ones directly through the Open WebUI settings interface, or run the docker exec -it ollama ollama pull <model-name> command in your server’s terminal.
Q: Can I use this stack completely offline without an internet connection?
A: Yes. Once you have downloaded the Docker images and your desired LLM models, the entire stack runs locally. No outbound internet connection is required for chat or document parsing.
Q: How much VRAM is recommended to run this setup smoothly?
A: A GPU with at least 8GB of VRAM (like an RTX 3060 or 4060) is recommended to run 7B or 8B parameter models. For larger 14B or 27B models, 16GB to 24GB of VRAM is required for smooth, high-speed generation.