Video models ·

Wan 2.2 S2V 14B (speech to video)

Backbone
16.3B
Native output
832×480, 81 frames
Default steps
40
License
apache-2.0
Released
2025-08

How to run it

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-S2V-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-S2V-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 2 days ago against Hugging Face, Ollama and GitHub.

Apps that run Wan 2.2 S2V 14B (speech to video)

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

Windows PC, NVIDIA GPU

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

Linux, NVIDIA or AMD GPU

ComfyUI

Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Also good:

App Type Opens it as Systems Engine Start
ComfyUI Desktop app Original or GGUF macOSWindowsLinux PyTorch, GGUF via ComfyUI-GGUF Get ComfyUI
SwarmUI Desktop app Original or GGUF macOSWindowsLinux ComfyUI Get SwarmUI
SD.Next Desktop app Original or GGUF macOSWindowsLinux PyTorch Get SD.Next
Stability Matrix Desktop app Original or GGUF macOSWindowsLinux installs ComfyUI, Forge, InvokeAI, SwarmUI, SD.Next or Fooocus Get Stability Matrix
WanGP Desktop app Built-in support WindowsLinux PyTorch Get WanGP

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Peak GPU memory at the native output size. Offloading moves the text encoder, or the whole pipeline, to system RAM between steps.

Precision Everything on GPU Text encoder offloaded Sequential offload Download
FP16 50.4 GB 38.4 GB 17.0 GB Original weights (opens in a new tab)
FP8 28.1 GB 22.1 GB 11.3 GB Original weights (opens in a new tab)
Q8 GGUF 29.3 GB 23.3 GB 11.7 GB GGUF (opens in a new tab)
Q4 GGUF 21.6 GB 15.6 GB 9.0 GB GGUF (opens in a new tab)

Components

Best hardware for Wan 2.2 S2V 14B (speech to video)

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc B570 (launch price $219) at Q4 GGUF.

Quality Hardware Quant Memory Speed Runs
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Workstation EditionNVIDIA RTX PRO 6000 Blackwell FP8 28.1 GB / 96.0 GB 11.0 min Runs well
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H100 80GB SXMNVIDIA H100 80GB SXM FP16 50.4 GB / 80.0 GB 11.6 min Runs well
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H800 80GB SXMNVIDIA H800 80GB SXM FP16 50.4 GB / 80.0 GB 11.6 min Runs well
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GH200 Grace Hopper (96 GB GPU)NVIDIA GH200 Grace Hopper (96 GB GPU) FP16 50.4 GB / 96.0 GB 11.6 min Runs well
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 141GB SXMNVIDIA H200 141GB SXM FP16 50.4 GB / 141 GB 11.6 min Runs well
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA B200NVIDIA B200 FP16 50.4 GB / 180 GB 10.2 min Runs well
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H100 NVLNVIDIA H100 NVL FP8 28.1 GB / 94.0 GB 11.3 min Runs well
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Server EditionNVIDIA RTX PRO 6000 Blackwell Server Edition FP8 28.1 GB / 96.0 GB 11.3 min Runs well
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 NVLNVIDIA H200 NVL FP8 28.1 GB / 141 GB 11.3 min Runs well
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Founders EditionNVIDIA GeForce RTX 5090 FP8 28.1 GB / 32.0 GB 12.5 min Slowtight fit

Check it on your own hardware

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.

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