Wan 2.2 I2V A14B on NVIDIA RTX 6000 Ada Generation

Can the NVIDIA RTX 6000 Ada Generation run Wan 2.2 I2V A14B?

83 pts

Yes, it runs well

Runs well. At FP8 it needs about 38.3 GB of 48.0 GB usable, with an estimated speed of 22.3 min.

Best precision: FP8
Memory mode: Everything on GPU
Time per clip: 22.3 min
Output: 1280×720, 81 frames, 40 steps

Download Wan 2.2 I2V A14B

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.

huggingface.co/QuantStack/Wan2.2-I2V-A14B-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-I2V-A14B (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 A 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.	37.9 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	5.5 / 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
Total	83.4 / 100	83 ptsGreat (A) · 83 of 100 points: Model quality 37.9/45, Memory fit 25.0/25, Speed 5.5/15, 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 RTX 6000 Ada Generation

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	28.2 GB	48.0 GB	32.2 min	Sequential CPU offload	Runs well	68 ptsGood (B) · 68 of 100 points: Model quality 38.7/45, Memory fit 12.0/25, Speed 4.6/15, Memory use 13.5/15	Original weights (opens in a new tab)
`FP8`	38.3 GB	48.0 GB	22.3 min	Everything on GPU	Runs well	83 ptsGreat (A) · 83 of 100 points: Model quality 37.9/45, Memory fit 25.0/25, Speed 5.5/15, Memory use 15.0/15	Original weights (opens in a new tab)
`Q8 GGUF`	39.3 GB	48.0 GB	28.5 min	Everything on GPU	Runs well	83 ptsGreat (A) · 83 of 100 points: Model quality 38.3/45, Memory fit 25.0/25, Speed 4.9/15, Memory use 15.0/15	GGUF · folder (opens in a new tab)
`Q4 GGUF`	32.7 GB	48.0 GB	29.7 min	Everything on GPU	Runs well	80 ptsGreat (A) · 80 of 100 points: Model quality 36.0/45, Memory fit 25.0/25, Speed 4.8/15, Memory use 15.0/15	GGUF · folder (opens in a new tab)

Other hardware that runs it

83 ptsGreat (A) · 83 of 100 points: Model quality 38.7/45, Memory fit 25.0/25, Speed 5.9/15, Memory use 13.9/15 NVIDIA RTX PRO 6000 Blackwell FP16 · 19.5 min
81 ptsGreat (A) · 81 of 100 points: Model quality 38.7/45, Memory fit 25.0/25, Speed 3.6/15, Memory use 13.9/15 AMD Ryzen AI Max+ 395 (128 GB) FP16 · 51.2 min
80 ptsGreat (A) · 80 of 100 points: Model quality 38.3/45, Memory fit 25.0/25, Speed 1.9/15, Memory use 15.0/15 Apple M4 Max (64 GB) Q8 GGUF · 3.0 h
79 ptsGreat (A) · 79 of 100 points: Model quality 38.3/45, Memory fit 25.0/25, Speed 1.6/15, Memory use 15.0/15 Apple M1 Max (64 GB) Q8 GGUF · 4.6 h
79 ptsGreat (A) · 79 of 100 points: Model quality 38.7/45, Memory fit 25.0/25, Speed 2.1/15, Memory use 13.9/15 Apple M4 Max (128 GB) FP16 · 2.6 h
78 ptsGreat (A) · 78 of 100 points: Model quality 38.7/45, Memory fit 25.0/25, Speed 3.5/15, Memory use 11.6/15 NVIDIA GB10 (128 GB) FP16 · 55.6 min

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.