What GPU do I need to run HiDream-ai/HiDream-I1-Full?
A 17.1B-parameter text-to-image model. 17.1B parameters, published in F16. View on Hugging Face
HiDream-I1-Full is published by HiDream-ai on Hugging Face, with 858 downloads and 1,000 likes to date. It's a unlisted-architecture model built for text-to-image, published natively in F16.
What HiDream-I1-Full is
HiDream-I1-Full is a 17.1B-parameter text-to-image model published by HiDream on Hugging Face, released under MIT.
License note: permissive: allows commercial use, modification and redistribution. Facts in this section are sourced from HiDream-I1-Full's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Text-to-image generation
- Creative asset generation
- ComfyUI workflows
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for activation memory and allocator fragmentation. Diffusion and video models carry no KV-cache. The real driver of extra memory is output resolution and frame count, which this flat overhead does not model. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP16 | 31.9 GB | 38.2 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | V100 | 3 | $0.264/hr | ||
| FP8 (quantized) | 15.9 GB | 19.1 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 8.0 GB | 9.6 GB | RTX 5060 Ti | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run HiDream-I1-Full at its published (F16) precision: 1× RTX A6000, at $0.363/hr per GPU ($0.363/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
HiDream-I1-Full: common questions
Can HiDream-I1-Full run on a single GPU?
Yes, but not on a desktop card. At FP16 it needs 38.2 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX A6000 at $0.363/hr.
What is the least VRAM HiDream-I1-Full can run in?
9.6 GB, at INT4 (quantized), which fits a 12 GB card, against 38.2 GB at FP16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing HiDream-I1-Full lower the GPU bill?
Yes. At FP16 the cheapest live fit is one RTX A6000 at $0.363/hr. At INT4 (quantized) it drops to one RTX 5060 Ti at $0.110/hr, provided a quantized checkpoint exists for it.
How to run HiDream-I1-Full
Run HiDream-I1-Full 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("HiDream-ai/HiDream-I1-Full", torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = pipe("a description of the scene").images[0]
image.save("output.png")Run HiDream-I1-Full with ComfyUI
Aquanode's ComfyUI template comes with ComfyUI preinstalled. Download HiDream-ai/HiDream-I1-Full'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 HiDream-I1-Full on Aquanode
Aquanode has no one-click deploy template for HiDream-I1-Full; 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 templateWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More HiDream-ai models
- HiDream-I1-Fast (17.1B, BF16)
Related reading: RTX A6000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
