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Wan-Dancer-14B

Wan-AI/Wan-Dancer-14B

Wan-Dancer-14B at Q4_K_M is exactly 26,083,251,744 bytes (24.29 GiB / 26.08 GB) — an effective 12.100 bits per weight, not the nominal 4.

From the file· summed from 3 file(s)From the file· KV per layer
Parameters
17.2B
Architecture
wan
null layers
Context
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q3_K_M2 shards15.83 GiB16,997,008,8967.885modulsx
Q4_K_S2 shards19.43 GiB20,867,865,0889.681modulsx
Q5_K_M2 shards23.98 GiB25,747,823,10411.944modulsx
Q4_K_M3 shards24.29 GiB26,083,251,74412.100modulsx
Q6_K2 shards26.91 GiB28,897,783,29613.406modulsx

Pipeline components

a diffusion model is a graph of parts, not one file
ComponentSizeShareCan live on the CPU?
denoiser79.74 GiB100%no, must be resident
Full pipeline79.74 GiBresident if nothing is offloaded

The parameter count published for a diffusion model describes the denoiser alone. Running it also requires its text encoder and VAE, and the text encoder is often nearly as large as the denoiser — which is why offloading it is the standard first move when you run out of memory.

We publish component sizes here, not throughput. Community-submitted image-generation rates do exist for many GPUs and we show them on the hardware pages, but they aggregate runs at different resolutions, step counts and settings, so they cannot be attributed to one model. Peak memory during sampling is unmeasured by any public source, and we do not estimate it.

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 9.03 GiB. The real file is 24.29 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
Attention heads
KV heads
Head dim
Hidden size
Vocab
Sliding window
none
SWA period
MLA
no
Experts
Experts per token
use_sliding_window

Questions people ask

How much VRAM does Wan-Dancer-14B need?
Q4_K_M is exactly 26,083,251,744 bytes (24.29 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of Wan-Dancer-14B should I use?
Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.