What GPU do I need to run Wan-AI/Wan2.2-S2V-14B?
16.3B parameters, published in BF16. View on Hugging Face
Set up Wan2.2-S2V-14BWan2.2-S2V-14B is published by Wan-AI on Hugging Face, with 18,206 downloads and 463 likes to date. It's a unlisted-architecture model built for image-to-video, published natively in BF16.
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 | 30.4 GB | 36.4 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| FP8 (quantized) | 15.2 GB | 18.2 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 7.6 GB | 9.1 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 Wan2.2-S2V-14B 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.
Wan2.2-S2V-14B: common questions
Can Wan2.2-S2V-14B run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 36.4 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 Wan2.2-S2V-14B can run in?
9.1 GB, at INT4 (quantized), which fits a 12 GB card, against 36.4 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 Wan2.2-S2V-14B 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 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 Wan2.2 models
- Wan2.2-S2V-14B-Diffusers (16.3B, BF16)
- Wan2.2-I2V-A14B-Diffusers (14.3B, F32)
- Wan2.2-I2V-A14B-Lightning-Diffusers (14.3B, BF16)
- Wan2.2-T2V-A14B-Diffusers (14.3B, F32)
- Wan2.2-TI2V-5B-Diffusers (5.0B, F32)
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.
