unsloth / text-to-image Open weights updated 1 week ago

Z-Image-Turbo-GGUF

Name: Z-Image-Turbo-GGUF
Author: unsloth

> [!NOTE] > This is a GGUF quantized version of Z-Image-Turbo. > unsloth/Z-Image-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision.

Params

6.2 B

Context

—

Downloads 30d

963 K

Likes

300

Commercial use: allowed apache-2.0 Not gated GGUF 1 languages View on Hugging Face ↗

Download history

daily snapshots · 106 days

▲ 3 K in the last 30 days (0.3%)

1.1 M49 K

Jul 28Sep 1Oct 6Nov 10

Can you run it?

Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.

File	Quant	Size	Est. VRAM	Verdict on RTX 4090 · 24 GB
z-image-turbo-Q2_K.gguf	Q2_K	2.3 GB	4.0 GB	✅ Runs comfortably
z-image-turbo-Q3_K_M.gguf	Q3_K_M	2.7 GB	4.4 GB	✅ Runs comfortably
z-image-turbo-Q4_K_M.gguf	Q4_K_M	3.9 GB	5.7 GB	✅ Runs comfortably
z-image-turbo-Q3_K_S.gguf	Q3_K_S	4.0 GB	5.8 GB	✅ Runs comfortably
z-image-turbo-Q4_0.gguf	Q4_0	4.6 GB	6.5 GB	✅ Runs comfortably
z-image-turbo-Q4_K_S.gguf	Q4_K_S	4.7 GB	6.6 GB	✅ Runs comfortably
z-image-turbo-Q4_1.gguf	Q4_1	4.9 GB	6.8 GB	✅ Runs comfortably
z-image-turbo-BF16.gguf	GGUF	12.3 GB	15.0 GB	✅ Runs comfortably

Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.

Specifications

Parameters: 6.2 B
Licence: apache-2.0
First seen on the Hub: 2025-12-21
Base model: Z-Image-Turbo
Training datasets: undisclosed
Added to our catalog: 2026-07-28

Family

Base model and the most-downloaded derivatives in the catalog.

Base Z-Image-Turbo Tongyi-MAI 715 K

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Appears in

Commercial-use image models