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 A5000 | 2 | $0.352/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 A4000 | 1 | $0.167/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: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Multi-head attention: every layer caches keys and values for every head. Cached per token: 819,200 bytes at 16-bit.
The model's maximum context length is not published in its configuration, so no per-context figures are shown.
Computed from the layer, head and window counts in the model's own configuration (Wan-AI/Wan2.2-S2V-14B), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
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 A4000 at $0.167/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)
Lineage
Quantized and fine-tuned versions
Alternatives at this size
Other models for image-to-video within about a third of Wan2.2-S2V-14B's 16.3B parameters, from other model lines.
- LTX-2 (18.9B, BF16)
- lingbot-world-fast (18.5B, F32)
- Cosmos-H-Surgical (15.2B, BF16)
More on Wan2.2-S2V-14B
Fits on a 48 GB GPU at BF16: every model that fits in 48 GB.
Fits on a 24 GB GPU at FP8: every model that fits in 24 GB.
Fits on a 12 GB GPU at INT4: every model that fits in 12 GB.
Best video generation models: how Wan2.2-S2V-14B ranks against the rest.
Related reading: RTX A6000 pricing and specs, Fast diffusion inference on GPU VMs, and What AI inference is.
