Open the configuration UI after installation:
- CPU package:
qvac.dappnode - GPU package:
qvac-gpu.dappnode
On the hostname for your installed package, the configuration UI uses port 8080, the OpenAI-compatible API uses /v1 on port 11434, and the interactive API documentation is at /docs on port 11434.
QVAC starts with the compact qwen3-600m model. Its first download can take several minutes; later starts reuse the persistent model cache.
The qvac.dnp.dappnode.eth package does not reserve a GPU and therefore starts on CPU-only DAppNodes. CPU inference is slower and needs enough system RAM for the selected model; begin with qwen3-600m before loading larger models.
The qvac-gpu.dnp.dappnode.eth package reserves every GPU exposed by Docker. Before installing it, confirm that docker run --rm --gpus all ubuntu:24.04 true succeeds on the DAppNode host. Use the CPU package if that check fails.
- Open Models in the configuration UI.
- Select Browse library.
- Search or filter the installed QVAC catalogue and add the models you want.
- Review which models are defaults or preload at startup.
- Select Save & reload.
Models that do not preload are loaded when first requested. This helps avoid filling the node's memory when several models are configured. Advanced users can add a model URL or local path, edit the complete JSON, or import an existing qvac.config.json from the same page.
Use the API key chosen during installation as a Bearer token. Omit the Authorization header if no key was configured.
QVAC_HOST=qvac.dappnode # Use qvac-gpu.dappnode for the GPU package
curl "http://${QVAC_HOST}:11434/v1/chat/completions" \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-d '{
"model": "qwen3-600m",
"messages": [{"role": "user", "content": "Explain DAppNode in one sentence."}]
}'The UI remains available while the QVAC worker reloads or downloads models. It also validates every save and keeps recent configuration backups.