What GPU do I need to run magespace/Wan2.2-I2V-A14B-Lightning-Diffusers?
14.3B parameters, published in BF16. View on Hugging Face
Set up Wan2.2-I2V-…htning-DiffusersWan2.2-I2V-A14B-Lightning-Diffusers is published by magespace on Hugging Face, with 45,873 downloads and 3 likes to date. It's a unlisted-architecture model built for text-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 | 26.6 GB | 31.9 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 3060 | 3 | $0.330/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 3070 | 1 | $0.088/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-I2V-A14B-Lightning-Diffusers at its published (BF16) precision: 1× RTX 4080 Super, at $0.338/hr per GPU ($0.338/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-I2V-A14B-Lightning-Diffusers: common questions
Does Wan2.2-I2V-A14B-Lightning-Diffusers fit on a 32 GB GPU?
Yes. At BF16 it needs 31.9 GB of VRAM, so a 32 GB card holds it with 0.1 GB to spare. A 24 GB card is not enough for it at BF16.
What is the least VRAM Wan2.2-I2V-A14B-Lightning-Diffusers can run in?
8.0 GB, at INT4 (quantized), which fits an 8 GB card, against 31.9 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-I2V-A14B-Lightning-Diffusers lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX 4080 Super at $0.338/hr. At INT4 (quantized) it drops to one RTX 3070 at $0.088/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More magespace models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
