What GPU do I need to run Wan-AI/Wan2.1-T2V-14B?
14.3B parameters, published in F32. View on Hugging Face
Set up Wan2.1-T2V-14BWan2.1-T2V-14B is published by Wan-AI on Hugging Face, with 34,359 downloads and 1,554 likes to date. It's a unlisted-architecture model built for text-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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 53.2 GB | 63.9 GB | A100 | 1 | $1.31/hr |
| cheaper alt. | V100 | 4 | $0.352/hr | ||
| FP8 (quantized) | 13.3 GB | 16.0 GB | RTX 5060 Ti | 1 | $0.188/hr |
| INT4 (quantized) | 6.7 GB | 8.0 GB | RTX 3060 | 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 Wan2.1-T2V-14B at its published (F32) precision: 1× A100, at $1.31/hr per GPU ($1.31/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Wan2.1-T2V-14B: common questions
Can Wan2.1-T2V-14B run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 63.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.31/hr.
Can Wan2.1-T2V-14B run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 53.2 GB, or 63.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 26.6 GB, or 31.9 GB with overhead. That moves it onto a 32 GB card, which the FP32 weights do not fit. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM Wan2.1-T2V-14B can run in?
8.0 GB, at INT4 (quantized), which fits an 8 GB card, against 63.9 GB at FP32. 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 Wan2.1-T2V-14B lower the GPU bill?
Yes. At FP32 the cheapest live fit is one A100 at $1.31/hr. At INT4 (quantized) it drops to one RTX 3060 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 Wan2.1 models
- Wan2.1-T2V-14B-Diffusers (14.3B, F32)
- Wan2.1-I2V-14B-480P (16.4B, F32)
- Wan2.1-I2V-14B-720P (16.4B, F32)
- Wan2.1-I2V-14B-720P-Diffusers (16.4B, F32)
- Wan2.1-I2V-14B-480P-Diffusers (16.4B, F32)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
