What GPU do I need to run Rabinovich/LongLive-2.0-5B-Diffusers?
5.0B parameters, published in BF16. View on Hugging Face
Set up LongLive-2.0-5B-DiffusersLongLive-2.0-5B-Diffusers is published by Rabinovich on Hugging Face, with 15,648 downloads and 0 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 | 9.3 GB | 11.2 GB | RTX 5060 Ti | 1 | $0.110/hr |
| FP8 (quantized) | 4.7 GB | 5.6 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 2.3 GB | 2.8 GB | RTX 5060 Ti | 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 LongLive-2.0-5B-Diffusers at its published (BF16) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
LongLive-2.0-5B-Diffusers: common questions
Does LongLive-2.0-5B-Diffusers fit on a 12 GB GPU?
Yes. At BF16 it needs 11.2 GB of VRAM, so a 12 GB card holds it with 0.8 GB to spare. An 8 GB card is not enough for it at BF16.
What is the least VRAM LongLive-2.0-5B-Diffusers can run in?
2.8 GB, at INT4 (quantized), which fits a 6 GB card, against 11.2 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Wan2.2 models
- Wan2.2-TI2V-5B-Diffusers (5.0B, F32)
- Wan2.2-T2V-A14B-Diffusers (14.3B, F32)
- Wan2.2-I2V-A14B-Diffusers (14.3B, F32)
- Wan2.2-I2V-A14B-Lightning-Diffusers (14.3B, BF16)
- Wan2.2-S2V-14B (16.3B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
