What GPU do I need to run krea/Krea-2-Raw?
12.8B parameters, published in BF16. View on Hugging FaceGated Full specs & deploy guide
Set up Krea-2-RawKrea-2-Raw is published by krea on Hugging Face, with 75,613 downloads and 578 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 A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 3060 | 3 | $0.330/hr | ||
| FP8 (quantized) | 11.9 GB | 14.3 GB | RTX 5060 Ti | 1 | $0.188/hr |
| INT4 (quantized) | 6.0 GB | 7.2 GB | RTX 3070 | 1 | $0.088/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-Raw at its published (BF16) 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.
Krea-2-Raw: common questions
Does Krea-2-Raw 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-Raw?
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-Raw 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-Raw lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A6000 at $0.363/hr. At INT4 (quantized) it drops to one RTX 3070 at $0.088/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-Turbo (12.8B, 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)
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.
