What GPU do I need to run Wan-AI/Wan2.1-T2V-1.3B-Diffusers?
1.4B parameters, published in F32. View on Hugging Face
Set up Wan2.1-T2V-1.3B-DiffusersWan2.1-T2V-1.3B-Diffusers is published by Wan-AI on Hugging Face, with 241,797 downloads and 150 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 | 5.3 GB | 6.3 GB | P4 | 1 | $0.069/hr |
| FP8 (quantized) | 1.3 GB | 1.6 GB | RTX 5060 Ti | 1 | $0.160/hr |
| INT4 (quantized) | 0.7 GB | 0.8 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-T2V-1.3B-Diffusers at its published (F32) precision: 1× P4, at $0.069/hr per GPU ($0.069/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-T2V-1.3B-Diffusers: common questions
Does Wan2.1-T2V-1.3B-Diffusers fit on a 8 GB GPU?
Yes. At FP32 it needs 6.3 GB of VRAM, so an 8 GB card holds it with 1.7 GB to spare. A 6 GB card is not enough for it at FP32.
Can Wan2.1-T2V-1.3B-Diffusers run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 5.3 GB, or 6.3 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 2.6 GB, or 3.2 GB with overhead. That moves it onto a 6 GB card instead of an 8 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM Wan2.1-T2V-1.3B-Diffusers can run in?
0.8 GB, at INT4 (quantized), which fits a 6 GB card, against 6.3 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Wan2.1 models
- Wan2.1-T2V-1.3B (1.4B, F32)
- TurboWan2.1-T2V-1.3B-Diffusers (1.4B, BF16)
- Wan2.1-VACE-1.3B-diffusers (2.2B, F32)
- Wan2.1-T2V-14B-Diffusers (14.3B, F32)
- Wan2.1-T2V-14B (14.3B, F32)
Alternatives at this size
Other models for text-to-video within about a third of Wan2.1-T2V-1.3B-Diffusers's 1.4B parameters, from other model lines.
- CogVideoX-2b (1.7B, F16)
- text-to-video-ms-1.7b (1.4B, F32)
- LTX-Video-0.9.5 (1.9B, BF16)
- i2vgen-xl (1.4B, F32)
More on Wan2.1-T2V-1.3B-Diffusers
Fits on an 8 GB GPU at FP32: every model that fits in 8 GB.
Best video generation models: how Wan2.1-T2V-1.3B-Diffusers ranks against the rest.
Related reading: H100 pricing and specs, Fast diffusion inference on GPU VMs, and What AI inference is.
