Studio
Getting Started
Run Hanzo Studio locally, submit your first prompt, and scale to GPU.
Getting Started
Hanzo Studio is a visual, node-based AI engine. Start on CPU for prototyping, then switch to GPU with one API call when you need throughput.
Run locally
docker pull ghcr.io/hanzoai/studio:latest
docker run -p 8188:8188 ghcr.io/hanzoai/studio:latest \
python main.py --listen 0.0.0.0 --cpu
# Open http://localhost:8188The editor opens in your browser. Drag nodes onto the canvas, wire them together, and press Queue Prompt to run the graph.
Authenticate
Studio authenticates against Hanzo IAM. Get an API key at console.hanzo.ai and pass it as a bearer token:
curl -H "Authorization: Bearer $HANZO_API_KEY" \
https://studio.hanzo.ai/v1/studio/prompts \
-d '{"workflow": { /* ... */ }}'One credential works across every Hanzo service. The same $HANZO_API_KEY
authenticates Studio, Chat, the gateway, and the rest.
Scale to GPU
Each organization has a compute profile. Switch it at any time — the coordinator routes GPU-heavy prompts to dedicated workers.
| Profile | GPU | VRAM | Best for |
|---|---|---|---|
cpu | — | — | Prototyping |
gpu-basic | T4 | 16 GB | Standard diffusion |
gpu-pro | A100 | 80 GB | Video, 3D, large models |
gpu-max | H100 | 80 GB | Maximum throughput |
Next steps
- Browse the API reference — generated from the OpenAPI spec.
- Every ComfyUI custom node works unmodified; the ecosystem manual is mirrored under Open Source → ComfyUI.
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