What GPU do I need to run krea/Krea-2-Turbo?
12.8B parameters, published in BF16. View on Hugging FaceGated
Set up Krea-2-TurboKrea-2-Turbo is published by krea on Hugging Face, with 77,774 downloads and 985 likes to date. It's a unlisted-architecture model built for text-to-image, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
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) |
|---|---|---|---|---|---|
| BF16 | 23.9 GB | 28.7 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 5060 Ti | 2 | $0.220/hr | ||
| FP8 (quantized) | 11.9 GB | 14.3 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 6.0 GB | 7.2 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 Krea-2-Turbo at its published (BF16) precision: 1× RTX 4080 Super, at $0.338/hr per GPU ($0.338/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Krea-2-Turbo: common questions
Does Krea-2-Turbo fit on a 32 GB GPU?
Yes. At BF16 it needs 28.7 GB of VRAM, so a 32 GB card holds it with 3.3 GB to spare. A 24 GB card is not enough for it at BF16.
Do I need approval to download Krea-2-Turbo?
Yes. krea gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 28.7 GB the model needs once you have them.
What is the least VRAM Krea-2-Turbo can run in?
7.2 GB, at INT4 (quantized), which fits an 8 GB card, against 28.7 GB at BF16. 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 Krea-2-Turbo lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 4080 Super at $0.338/hr. At FP8 (quantized) it drops to one RTX 5060 Ti 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 krea models
- Krea-2-Raw (12.8B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
