Video models · Alibaba

Wan-Dancer 14B (music to dance)

Name: Wan-Dancer 14B (music to dance)
Author: Alibaba

Backbone: 17.3B
Native output: 720×1280, 149 frames
Default steps: 48
License: apache-2.0
Released: 2026-07

How to run it

Original weights · Hugging Face

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

huggingface.co/Wan-AI/Wan-Dancer-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/Wan-Dancer (opens in a new tab)

git clone https://github.com/Wan-Video/Wan-Dancer

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

Apps that run Wan-Dancer 14B (music to dance)

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)

Draw Things

Native Mac app with its own fast Metal engine and quantized downloads.

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	Built-in support	macOSWindowsLinux	PyTorch, GGUF via ComfyUI-GGUF	Get ComfyUI
Draw Things	Desktop app	Built-in support	macOSiOS	Draw Things	Open in Draw Things
SwarmUI	Desktop app	Built-in support	macOSWindowsLinux	ComfyUI	Get SwarmUI
SD.Next	Desktop app	Built-in support	macOSWindowsLinux	PyTorch	Get SD.Next
Stability Matrix	Desktop app	Built-in support	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`	77.0 GB	64.3 GB	41.6 GB	Original weights (opens in a new tab)
`FP8`	53.4 GB	47.1 GB	35.6 GB	Original weights (opens in a new tab)

Components

Backbone: 17.25B
VAE: 0.127B
umT5-XXL: 5.7B
CLIP ViT-H (image): 0.63B

Best hardware for Wan-Dancer 14B (music to dance)

The least expensive GPU in our catalog that runs it without offloading is the AMD Radeon PRO W7800 48GB (launch price $2,799) at FP8.

Quality	Hardware	Quant	Memory	Speed	Runs
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX PRO 6000 Blackwell	`FP16`	77.0 GB / 96.0 GB	53.7 min	Very slow
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	AMD Ryzen AI Max+ 395 (128 GB)	`FP16`	77.0 GB / 96.0 GB	2.3 h	Very slow
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GB10 (128 GB)	`FP16`	77.0 GB / 115 GB	2.5 h	Very slow
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA RTX Spark N1X (6144-core GPU) (128 GB)	`FP16`	77.0 GB / 102 GB	2.7 h	Very slow
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.	Apple M4 Max (128 GB)	`FP16`	77.0 GB / 96.0 GB	7.2 h	Very slow
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	Apple M2 Ultra (192 GB)	`FP16`	77.0 GB / 144 GB	5.3 h	Very slow
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.	Apple M3 Ultra (256 GB)	`FP16`	77.0 GB / 192 GB	5.2 h	Very slow
76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.	Apple M3 Ultra (512 GB)	`FP16`	77.0 GB / 384 GB	5.2 h	Very slow
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 Max (64 GB)	`FP8`	35.6 GB / 48.0 GB	8.4 h	Very slowCPU offload
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.	Apple M1 Max (64 GB)	`FP8`	35.6 GB / 48.0 GB	12.9 h	Very slowCPU offload

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.