What GPU do I need to run Wan-AI/Wan2.2-T2V-A14B-Diffusers?
A 14.3B-parameter text-to-video MoE diffusion model. 14.3B parameters, published in F32. View on Hugging Face
Wan2.2-T2V-A14B-Diffusers is published by Wan-AI on Hugging Face, with 147,446 downloads and 157 likes to date. It's a unlisted-architecture model built for text-to-video, published natively in F32.
What Wan2.2-T2V-A14B is
Wan2.2-T2V-A14B is a text-to-video model from Wan-AI's Wan2.2 release: a Mixture-of-Experts video diffusion architecture that splits the denoising process across timesteps between specialized expert models, generating 5-second clips at 480P and 720P. Wan2.2 is trained on substantially more image and video data than Wan2.1 (its own card states +65.6% more images, +83.2% more videos).
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from Wan2.2-T2V-A14B's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Text-to-video generation
- Short-form creative video (5s clips, 480P/720P)
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 | 53.2 GB | 63.9 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | V100 | 4 | $0.352/hr | ||
| FP8 (quantized) | 13.3 GB | 16.0 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 6.7 GB | 8.0 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 Wan2.2-T2V-A14B-Diffusers at its published (F32) precision: 1× A100, at $1.21/hr per GPU ($1.21/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-T2V-A14B-Diffusers: common questions
Can Wan2.2-T2V-A14B-Diffusers run on a single GPU?
Yes, but not on a desktop card. At FP32 it needs 63.9 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
Can Wan2.2-T2V-A14B-Diffusers run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 53.2 GB, or 63.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 26.6 GB, or 31.9 GB with overhead. That moves it onto a 32 GB card, which the FP32 weights do not fit. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM Wan2.2-T2V-A14B-Diffusers can run in?
8.0 GB, at INT4 (quantized), which fits an 8 GB card, against 63.9 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-T2V-A14B-Diffusers lower the GPU bill?
Yes. At FP32 the cheapest live fit is one A100 at $1.21/hr. At FP8 (quantized) it drops to one RTX 5060 Ti at $0.110/hr, provided a quantized checkpoint exists for it.
How to run Wan2.2-T2V-A14B
Run Wan2.2-T2V-A14B with Diffusers (Python)
From Wan-AI/Wan2.2-T2V-A14B-Diffusers's own model card (requires diffusers installed from source as of this model's release).
import torch
from diffusers import WanPipeline, AutoencoderKLWan
from diffusers.utils import export_to_video
model_id = "Wan-AI/Wan2.2-T2V-A14B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
pipe.to("cuda")
output = pipe(prompt="a description of the scene", height=720, width=1280, num_frames=81).frames[0]
export_to_video(output, "t2v_out.mp4", fps=16)Source: https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers/raw/main/README.md
Run Wan2.2-T2V-A14B with ComfyUI
Aquanode's ComfyUI template comes with ComfyUI preinstalled. Download Wan-AI/Wan2.2-T2V-A14B's checkpoint into the models folder and load it in a workflow; this is a real Aquanode template, but loading this specific checkpoint is a manual step, not a one-click deploy.
Deploy Wan2.2-T2V-A14B on Aquanode
Aquanode has no one-click deploy template for Wan2.2-T2V-A14B; it comes with ComfyUI preinstalled, so you only need to load the checkpoint, not install anything. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch the ComfyUI template sized to the requirement above (1× A100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch the ComfyUI templateWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Wan2.2 models
- Wan2.2-I2V-A14B-Diffusers (14.3B, F32)
- Wan2.2-I2V-A14B-Lightning-Diffusers (14.3B, BF16)
- Wan2.2-S2V-14B (16.3B, BF16)
- Wan2.2-S2V-14B-Diffusers (16.3B, BF16)
- Wan2.2-TI2V-5B-Diffusers (5.0B, F32)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
