Image generation
How to deploy FLUX.2-dev on a GPU cloud
A 32.2B-parameter text-to-image model. Full specs, license and use cases.
FLUX.2-dev size and hardware requirements
32.2B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
72.0 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 60.0 GB | 72.0 GB | A100 | 1 | $0.851/hr |
| FP8 (quantized) | 30.0 GB | 36.0 GB | RTX 6000 Ada | 1 | $0.524/hr |
| INT4 (quantized) | 15.0 GB | 18.0 GB | RTX 3090 | 1 | $0.147/hr |
How to run FLUX.2-dev
Run FLUX.2-dev 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("black-forest-labs/FLUX.2-dev", torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = pipe("a description of the scene").images[0]
image.save("output.png")Run FLUX.2-dev with ComfyUI
Aquanode's ComfyUI template comes with ComfyUI preinstalled. Download black-forest-labs/FLUX.2-dev'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 FLUX.2-dev on Aquanode
Aquanode has no one-click deploy template for FLUX.2-dev; 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× A100 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