Docker Model Runner vs Ollama — ใครควรย้าย ใครไม่ควร (และทำไม)

Docker Model Runner and Ollama are two tools that allow users to run large language models (LLMs) on their computers. When deciding whether to switch from Ollama to Docker Model Runner, or vice versa, there are several factors to consider. The choice ultimately depends on whether you already use Docker and your specific use case.

Docker Model Runner, which was released in April 2025, views LLMs as container artifacts. This means that models should be managed similarly to container images – versioned, tagged, and pushable – and exist within the same ecosystem as Docker. In contrast, Ollama sees LLMs as native apps, requiring only one command to install and run without the need to understand containers or configure anything.

Installing Docker Model Runner is as simple as using Docker Desktop and opening the AI tab. No additional software is required. Ollama, on the other hand, can be installed using a single command with curl, and it does not require any additional software either.

The two tools differ in their ecosystems. Docker Model Runner uses OCI artifacts stored on Docker Hub, while Ollama has its own registry. Both tools support OpenAI-compatible APIs, but Docker Model Runner also includes Ollama-compatible APIs, making it easier to integrate existing Ollama-based applications.

Both tools support automatic GPU management using Metal on Apple Silicon and CUDA on NVIDIA hardware. Docker Model Runner supports the llama.cpp, vLLM, and Diffusers engines out of the box, while Ollama uses llama.cpp by default.

Performance-wise, the difference between the two tools is minimal – only about 12%. Docker Model Runner is slightly faster (1.00 to 1.12x) due to minimal overhead from containerization. However, this performance difference may not justify the added complexity for most users.

Docker Model Runner's main advantage is that models are stored as OCI artifacts – versioned, tagged, pushable, and reproducible. This makes them more similar to container images, which can be easily managed and shared. Docker Compose also allows you to declare the model as a dependency in your docker-compose.yml file, making it an integral part of your application stack.

The ecosystem around Docker Model Runner is still growing, with support from major players like Google, Hugging Face, and VMware. Ollama, on the other hand, has a larger community and more community models, as well as integrations with popular tools like LangChain, CrewAI, Open WebUI, Continue, and Cody. Ollama also has a wide range of tutorials available.

If you encounter issues, chances are that others have faced and resolved them before with Ollama. Docker Model Runner, being a newer tool, may present unique challenges, such as dealing with Modelfile configuration.

In conclusion, choosing between Docker Model Runner and Ollama depends on your familiarity with Docker and your specific use case. If you already use Docker and are looking for version-controlled, reproducible models and easier integration with other Docker tools, Docker Model Runner might be the better choice. However, if you prefer a simpler, more lightweight solution that doesn't require Docker knowledge, Ollama is likely the better option.