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Can the NVIDIA GeForce RTX 5090 run Wan 2.2 Animate 14B?

70 ptsGood (B) · 70 of 100 points: Model quality 32.6/45, Memory fit 18.0/25, Speed 4.5/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q4 GGUF it needs about 28.3 GB of 32.0 GB usable, with an estimated speed of 33.4 min.

Best precision
Q4 GGUF
Memory mode
Text encoder offloaded
Time per clip
33.4 min
Output
1280×720, 77 frames, 40 steps

Download Wan 2.2 Animate 14B

Recommended for the NVIDIA GeForce RTX 5090: q4_gguf. Every other precision has its own download link in the table below.

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

Download the q4_gguf (Q4_K_S) file (opens in a new tab)

huggingface.co/QuantStack/Wan2.2-Animate-14B-GGUF (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/Wan-AI/Wan2.2-Animate-14B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Alibaba. The weights themselves download from Hugging Face.

github.com/Wan-Video/Wan2.2 (opens in a new tab)

git clone https://github.com/Wan-Video/Wan2.2

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the B grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.32.6 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.18.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.4.5 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total70.1 / 10070 ptsGood (B) · 70 of 100 points: Model quality 32.6/45, Memory fit 18.0/25, Speed 4.5/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every precision on the NVIDIA GeForce RTX 5090

Precision Memory needed Available Time per output Memory mode Fit Grade · points Download
FP16 29.8 GB 32.0 GB 34.4 min Sequential CPU offload Tight fit 64 ptsGood (B) · 64 of 100 points: Model quality 35.1/45, Memory fit 10.0/25, Speed 4.4/15, Memory use 15.0/15 Original weights (opens in a new tab)
FP8 23.7 GB 32.0 GB 31.0 min Sequential CPU offload Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 34.4/45, Memory fit 12.0/25, Speed 4.7/15, Memory use 15.0/15 Original weights (opens in a new tab)
Q8 GGUF 24.1 GB 32.0 GB 39.6 min Sequential CPU offload Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 34.7/45, Memory fit 12.0/25, Speed 4.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4 GGUF 28.3 GB 32.0 GB 33.4 min Text encoder offloaded Tight fit 70 ptsGood (B) · 70 of 100 points: Model quality 32.6/45, Memory fit 18.0/25, Speed 4.5/15, Memory use 15.0/15 GGUF (opens in a new tab)

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.