Z-Image Turbo (6B) has 6 billion parameters. At standard 4-bit quantization with 1024×1024px, it needs roughly 8.8 GB of VRAM — weights plus cache and runtime overhead.
VRAM by quantization
| Precision | Weights | Cache/Buffer | Total VRAM |
|---|---|---|---|
| 2-bit (IQ2_XXS) | 1.9 GB | 3.5 GB | 7.4 GB |
| 4-bit (Q4_K_M) | 3.3 GB | 3.5 GB | 8.8 GB |
| 8-bit (Q8_0) | 6.3 GB | 3.5 GB | 11.8 GB |
| 16-bit (FP16) | 12.0 GB | 3.5 GB | 17.5 GB |
Which GPU can run Z-Image Turbo (6B) (at 4-bit)?
| GPU class | VRAM | Z-Image Turbo (6B) (8.8 GB) |
|---|---|---|
| 8 GB · RTX 5060 / 4060 | 8 GB | Tight |
| 12 GB · RTX 5070 / 3060 | 12 GB | Fits |
| 16 GB · RTX 5070 Ti / 4080 | 16 GB | Fits |
| 24 GB · RTX 4090 / 3090 | 24 GB | Fits |
| 32 GB · RTX 5090 | 32 GB | Fits |
| 48 GB · 2×24 / RTX 6000 Ada | 48 GB | Fits |
| 128 GB · M-series / RTX Spark | 128 GB | Fits |
Alibaba's fast photorealistic image model for 16GB-class GPUs.
Get the exact number for your setup
Pick your model, quantization, and context length — the calculator shows the full VRAM math and tells you precisely which hardware fits.
Open the Local AI Calculator →
VRAM figures are reproducible estimates (weights + KV cache + overhead) and vary by runtime and quant format. Data current as of 2026-06-15.