What GPU do I need to run Wan-AI/Wan2.2-TI2V-5B-Diffusers?
5.0B parameters, published in F32. View on Hugging Face Full specs & deploy guide
Set up Wan2.2-TI2V-5B-DiffusersWan2.2-TI2V-5B-Diffusers is published by Wan-AI on Hugging Face, with 189,587 downloads and 160 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 | 18.6 GB | 22.4 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 4.7 GB | 5.6 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 2.3 GB | 2.8 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-TI2V-5B-Diffusers at its published (F32) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/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-TI2V-5B-Diffusers: common questions
Does Wan2.2-TI2V-5B-Diffusers fit on a 24 GB GPU?
Yes. At FP32 it needs 22.4 GB of VRAM, so a 24 GB card holds it with 1.6 GB to spare. A 16 GB card is not enough for it at FP32.
Can Wan2.2-TI2V-5B-Diffusers run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 18.6 GB, or 22.4 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 9.3 GB, or 11.2 GB with overhead. That moves it onto a 12 GB card instead of a 24 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM Wan2.2-TI2V-5B-Diffusers can run in?
2.8 GB, at INT4 (quantized), which fits a 6 GB card, against 22.4 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 Wan2.2-TI2V-5B-Diffusers lower the GPU bill?
Yes. At FP32 the cheapest live fit is one RTX A5000 at $0.176/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 Wan-AI models
- Wan2.1-T2V-1.3B-Diffusers (1.4B, F32)
- Wan2.2-T2V-A14B-Diffusers (14.3B, F32)
- Wan2.2-I2V-A14B-Diffusers (14.3B, F32)
- Wan2.1-I2V-14B-720P-Diffusers (16.4B, F32)
- Wan2.1-T2V-14B-Diffusers (14.3B, F32)
- Wan2.1-I2V-14B-480P-Diffusers (16.4B, F32)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
