Image generation
How to deploy Qwen-Image on a GPU cloud
A 20.4B-parameter text-to-image model. Full specs, license and use cases.
Qwen-Image size and hardware requirements
20.4B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
45.7 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 38.1 GB | 45.7 GB | RTX A6000 | 1 | $0.330/hr |
| FP8 (quantized) | 19.0 GB | 22.8 GB | RTX 4090 | 1 | $0.340/hr |
| INT4 (quantized) | 9.5 GB | 11.4 GB | RTX 4070 Super | 1 | $0.110/hr |
How to run Qwen-Image
Run Qwen-Image with Diffusers (Python)
Generic example using Hugging Face's diffusers library, not from the model's own docs.
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = pipe("a description of the scene").images[0]
image.save("output.png")Run Qwen-Image with ComfyUI
Aquanode's ComfyUI template comes with ComfyUI preinstalled. Download Qwen/Qwen-Image's checkpoint into the models folder and load it in a workflow; this is a real Aquanode template, but loading this specific checkpoint is a manual step, not a one-click deploy.
Deploy Qwen-Image on Aquanode
Aquanode has no one-click deploy template for Qwen-Image; it comes with ComfyUI preinstalled, so you only need to load the checkpoint, not install anything. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch the ComfyUI template sized to the requirement above (1× RTX A6000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch the ComfyUI template