What GPU do I need to run HiDream-ai/HiDream-I1-Full?
17.1B parameters, published in F16. View on Hugging Face Full specs & deploy guide
Set up HiDream-I1-FullHiDream-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.
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 | $0.330/hr |
| cheaper alt. | V100 | 3 | $0.180/hr | ||
| FP8 (quantized) | 15.9 GB | 19.1 GB | RTX 4000 SFF Ada | 1 | $0.180/hr |
| INT4 (quantized) | 8.0 GB | 9.6 GB | RTX 4070 Super | 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 $0.330/hr per GPU ($0.330/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 $0.330/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 $0.330/hr. At INT4 (quantized) it drops to one RTX 4070 Super at $0.110/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More HiDream-ai models
- HiDream-I1-Fast (17.1B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
