Wan-Dancer 14B (music to dance) on Apple M3 Pro (18 GB)

Can the Apple M3 Pro (18 GB) run Wan-Dancer 14B (music to dance)?

No, it doesn’t fit

No. Wan-Dancer 14B (music to dance) needs about 35.6 GB even at FP8; the Apple M3 Pro (18 GB) has 12.1 GB usable.

Download Wan-Dancer 14B (music to dance)

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 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP8, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Won't run: How well it runs on the Apple M3 Pro (18 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	10.1 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Match score	10.1 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every precision on the Apple M3 Pro (18 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	41.6 GB	12.1 GB	—	—	Won't run	76Great quality (A) · 76 of 100: how capable the model is at FP16, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	35.6 GB	12.1 GB	—	—	Won't run	74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Hardware that can run it

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 · 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 · 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 · 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 · 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 · 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 · 5.3 h · Very slow

Models that do run on the Apple M3 Pro (18 GB)

49Fair quality (C) · 49 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. LTX-Video 2B FP8 · 4.4 min
44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 2B FP8 · 10.4 min
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. Bernini-R A14B (video generation + editing) FP8 · 2.7 h
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5B FP8 · 32.9 min
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5B I2V FP8 · 32.9 min

Quick answers

How much memory does Wan-Dancer 14B (music to dance) need on the M3 Pro 18GB?

Even at FP8 it needs about 35.6 GB; the M3 Pro 18GB has 12.1 GB usable.

What can the M3 Pro 18GB run instead?

LTX-Video 2B, CogVideoX 2B and Bernini-R A14B (video generation + editing) run best on it; the page lists more, each with its speed.

What hardware can run Wan-Dancer 14B (music to dance)?

NVIDIA RTX PRO 6000 Blackwell 96GB, AMD Ryzen AI Max+ 395 128GB and NVIDIA GB10 128GB run it; the page lists more, each with its speed.

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.