What GPU do I need to run Wan-AI/Wan2.1-I2V-14B-480P?
16.4B parameters, published in F32. View on Hugging Face
Set up Wan2.1-I2V-14B-480PWan2.1-I2V-14B-480P is published by Wan-AI on Hugging Face, with 47,083 downloads and 229 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 61.1 GB | 73.3 GB | A100 | 1 | $1.16/hr |
| cheaper alt. | V100 | 5 | $0.550/hr | ||
| FP8 (quantized) | 15.3 GB | 18.3 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 7.6 GB | 9.2 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-I2V-14B-480P at its published (F32) precision: 1× A100, at $1.16/hr per GPU ($1.16/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-I2V-14B-480P: 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/Wan2.1-I2V-14B-480P), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Wan2.1-I2V-14B-480P: common questions
Can Wan2.1-I2V-14B-480P run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 73.3 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 $1.16/hr.
Can Wan2.1-I2V-14B-480P run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 61.1 GB, or 73.3 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 30.5 GB, or 36.6 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM Wan2.1-I2V-14B-480P can run in?
9.2 GB, at INT4 (quantized), which fits a 12 GB card, against 73.3 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-I2V-14B-480P lower the GPU bill?
Yes. At FP32 the cheapest live fit is one A100 at $1.16/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-I2V-14B-720P (16.4B, F32)
- Wan2.1-I2V-14B-720P-Diffusers (16.4B, F32)
- Wan2.1-I2V-14B-480P-Diffusers (16.4B, F32)
- Wan2.1-VACE-14B (17.3B, F32)
- Wan2.1-T2V-14B-Diffusers (14.3B, F32)
Alternatives at this size
Other models for image-to-video within about a third of Wan2.1-I2V-14B-480P's 16.4B parameters, from other model lines.
- LTX-2 (18.9B, BF16)
- lingbot-world-fast (18.5B, F32)
- Cosmos-H-Surgical (15.2B, BF16)
More on Wan2.1-I2V-14B-480P
Fits on an 80 GB GPU at FP32: every model that fits in 80 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 Wan2.1-I2V-14B-480P ranks against the rest.
Related reading: A100 pricing and specs, Fast diffusion inference on GPU VMs, and What AI inference is.
