local model runtimeDocker Model Runner: uses, limits and practical trial
Docker Model Runner manages and serves models through Docker using compatible APIs and registry-distributed formats.
Sources consulted on · Docker
Directory facts
- Publisher / organisation
- Docker
- Primary use
- local model runtime
- Related directory
- Explore this family’s services
Suitable tasks
Integrate identified inference into a development environment.
Limits and checks
Supported engines, platforms and accelerators differ; downloaded models retain their licence requirements.
A repeatable trial
Download an authorised model, record version and engine, then repeat one prompt after restart. Check API, memory and network access.
How to decide
Choose the deployment when reproducible and compatible with the target machine.
Frequently asked questions
Do all engines run on every platform?
No. Check current engine and accelerator requirements.
Official documentation and scope
Functions are described from documentation. Proposed trials are editorial advice, not executed benchmarks. Check prices, quotas, access and conditions before choosing.
Consultation covers identification and described functions; performance and all contractual conditions were not tested.
Alternatives and related reading
- Jan
- GPT4All
- LibreChat
- MLX LM
- SGLang
- PrivateGPT
- KoboldCpp
- llamafile
- TextGen (Text Generation WebUI)
- Docling
- Ollama
- LM Studio
- Open WebUI
- LocalAI
- vLLM
- AnythingLLM
- llama.cpp
- Local and open AI: verify the control you really get
- Test an AI inference server: workload, complete latency and accepted responses
- Using AI with confidential data: essential controls
- Build a reliable, citable RAG knowledge base
- Compare by output and actual cost