zai-org · video

SCAIL-Preview

zai-org/SCAIL-Preview

SCAIL-Preview at Q4_K_M is exactly 11,341,532,672 bytes (10.56 GiB / 11.34 GB) — an effective 5.534 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)
Parameters
16.4B
Architecture
wan
Context
native (config.json)
License
mit

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q2_K6.05 GiB6,499,241,4723.171vantagewithai
Q2_K6.05 GiB6,499,241,4723.171wanabmeya
Q3_K_S7.38 GiB7,926,287,8723.868wanabmeya
Q3_K_S7.38 GiB7,926,287,8723.868vantagewithai
Q3_K_M8.00 GiB8,587,382,2724.190vantagewithai
Q3_K_M8.00 GiB8,587,382,2724.190wanabmeya
Q4_09.54 GiB10,247,900,6725.000vantagewithai
Q4_09.54 GiB10,247,900,6725.000wanabmeya
Q4_K_S9.72 GiB10,437,955,0725.093wanabmeya
Q4_K_S9.72 GiB10,437,955,0725.093vantagewithai
Q4_110.32 GiB11,080,207,8725.407vantagewithai
Q4_110.32 GiB11,080,207,8725.407wanabmeya
Q4_K_M10.56 GiB11,341,532,6725.534vantagewithai
Q4_K_M10.56 GiB11,341,532,6725.534wanabmeya
Q5_K_S11.26 GiB12,089,462,2725.899wanabmeya
Q5_K_S11.26 GiB12,089,462,2725.899vantagewithai
Q5_011.42 GiB12,266,409,4725.986vantagewithai
Q5_011.42 GiB12,266,409,4725.986wanabmeya
Q5_K_M11.87 GiB12,744,003,0726.218wanabmeya
Q5_K_M11.87 GiB12,744,003,0726.218vantagewithai
Q5_112.20 GiB13,098,716,6726.392vantagewithai
Q5_112.20 GiB13,098,716,6726.392wanabmeya
Q6_K13.26 GiB14,234,127,8726.946vantagewithai
Q6_K13.26 GiB14,234,127,8726.946wanabmeya
Q8_016.90 GiB18,144,988,6728.854wanabmeya
Q8_016.90 GiB18,144,988,6728.854vantagewithai

Pipeline components

a diffusion model is a graph of parts, not one file
ComponentSizeShareCan live on the CPU?
denoiser2.83 GiB100%no, must be resident
Full pipeline2.83 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 8.59 GiB. The real file is 10.56 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

Architecture unavailable — this repository is gated and no ungated mirror was found. Exact file sizes above are still authoritative; only the KV math needs the config.

Questions people ask

How much VRAM does SCAIL-Preview need?
Q4_K_M is exactly 11,341,532,672 bytes (10.56 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of SCAIL-Preview 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.