What GPU do I need to run Wan-AI/Wan2.1-VACE-14B?
17.3B parameters, published in F32. View on Hugging Face
Wan2.1-VACE-14B is published by Wan-AI on Hugging Face, with 8,815 downloads and 503 likes to date. It's a unlisted-architecture model built for image-to-video, published natively in F32.
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.
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 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run Wan2.1-VACE-14B at its published (F32) precision: 1× A100 on vastai, at $1.15/hr per GPU ($1.15/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Wan-AI models
- Wan2.2-TI2V-5B-Diffusers (5.0B, F32)
- Wan2.1-T2V-1.3B-Diffusers (1.4B, F32)
- Wan2.2-I2V-A14B-Diffusers (14.3B, F32)
- Wan2.2-T2V-A14B-Diffusers (14.3B, F32)
- Wan2.1-T2V-14B-Diffusers (14.3B, F32)
- Wan2.1-I2V-14B-720P-Diffusers (16.4B, F32)
