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Docker Model Runner can pull models from Docker Hub, OCI-compatible registries or Hugging Face and serve them locally.
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Docker Model Runner: Windows/Linux GPU requirements, llama.cpp/vLLM/Diffusers, OpenAI-Ollama APIs, context/VRAM, OCI models and Ollama differences.
Docker Model Runner can pull models from Docker Hub, OCI-compatible registries or Hugging Face and serve them locally.
Current Docker docs document OpenAI- and Ollama-compatible APIs, while the API reference also documents Anthropic-compatible endpoints.
Supported inference engines include llama.cpp, vLLM and Diffusers; vLLM and Diffusers are positioned for Linux with NVIDIA GPUs.
For Windows amd64, Docker currently lists an NVIDIA GPU and driver 576.57+ as requirements.
Ollama made local inference easy; Docker Model Runner brings model distribution closer to Docker, registries and Compose. A key difference is treating models as versionable OCI artifacts that can be pulled, pushed and incorporated into deployment workflows.
DMR caches models locally after the first pull, loads them into memory when requests arrive, and can unload them when idle. Capacity planning should separate cold-load time from resident VRAM use.
Current Docker Desktop requirements for Windows amd64 list NVIDIA GPU and driver 576.57+. Docker Engine on Linux supports a broader set including CPU, NVIDIA CUDA, AMD ROCm and Vulkan, so platform must be stated when discussing AMD support.
Current DMR docs position vLLM and Diffusers on Linux with NVIDIA GPUs. On Windows, llama.cpp-backed GGUF inference is the more natural starting point.
nvidia-smidocker versionDMR uses `docker model` commands for model lifecycle. Pull a model from a registry, run it, inspect metadata/context capabilities and connect applications to the local API.
If `docker model` is not recognized, verify Docker version and that Model Runner is enabled. Prefer the official upgrade/enable path over copying arbitrary CLI plugins from the internet.
docker model --helpdocker model pull ai/qwen2.5-coderdocker model inspect ai/qwen2.5-coderWhen host TCP access is enabled in Docker Desktop, DMR APIs can be reached on localhost:12434 in the documented setup. OpenAI clients use an `/engines/v1` base path while Ollama-compatible clients can use the host endpoint, lowering integration cost for existing tools.
API compatibility does not mean every Ollama/OpenAI behavior is identical. Test tool calling, embeddings, model options and streaming with the specific client you plan to use.
curl http://localhost:12434/engines/v1/modelscurl http://localhost:12434/api/tagsModel weights stay the same, but longer context increases KV-cache and runtime-buffer memory. Docker supports `docker model configure --context-size`; reducing context is one of the first responses to out-of-memory errors.
Docker notes that a model's maximum supported context and configured runtime context are different. A model advertising 128K does not mean 128K is practical on an 8/12/16 GB GPU.
docker model configure --context-size 8192 ai/qwen2.5-coderdocker model inspect ai/qwen2.5-coderllama.cpp suits resource-efficient local GGUF inference. vLLM targets higher-throughput Safetensors serving. Diffusers serves text-to-image models such as Stable Diffusion families.
DMR should not be viewed only as another LLM runner. Its strategic difference is combining multiple inference engines and model-artifact workflows under Docker.
Ollama remains very practical for quick setup, community ecosystem, Modelfiles and a simple local API. DMR is especially attractive for Docker-centric teams that want registries, Compose, artifact distribution and consistent deployment workflows.
For a single Windows workstation running only chat/coding models, Ollama can be simpler. If you want model artifacts versioned with an app stack, shared through registries, moved to Linux GPU servers or served with Diffusers/vLLM, DMR may fit better.
Docker documents that on Linux the inference engines run inside containers, while on macOS and Windows they run in platform sandbox environments rather than ordinary containers. This matters for security architecture.
Model artifacts are supply-chain inputs just like software dependencies. Review registry provenance, model source, license and custom runtime flags before production deployment.
| Need | More natural fit |
|---|---|
| Fast single-user local chat/coding | Ollama |
| Model lifecycle with Compose/registries | Docker Model Runner |
| Linux high-throughput Safetensors serving | DMR + vLLM |
| Image generation in Docker workflow | DMR + Diffusers (Linux/NVIDIA) |
Before production changes, verify context and keep backups and a rollback plan. Do not change several DNS, TLS, recovery, Docker or WordPress variables at once because it obscures the root cause.
No. They overlap in local inference but optimize for different ecosystems and deployment workflows.
Current Docker Desktop Windows amd64 requirements list NVIDIA GPUs; AMD/ROCm is documented for Docker Engine on Linux.
No. Larger context consumes more memory and can reduce throughput. Use the smallest context that meets the workload.
If the problem persists in hosting, VPS, Docker, Cloudflare, Windows or WordPress infrastructure, open a technical support request with the exact error output and current architecture.