What GPU do I need to run FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers?
5.0B parameters, published in BF16. View on Hugging Face
FastWan2.2-TI2V-5B-FullAttn-Diffusers is published by FastVideo on Hugging Face, with 79,500 downloads and 67 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.
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 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run FastWan2.2-TI2V-5B-FullAttn-Diffusers at its published (BF16) precision: 1× RTX 3060 on simplepod, at $0.070/hr per GPU ($0.070/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More FastVideo models
- Qwen3-0.6B (752M, BF16)
- Qwen3-8B (8.2B, BF16)
- gpt2 (137M, F32)
- Qwen3.5-9B (9.7B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen2.5-7B-Instruct (7.6B, BF16)
