I ran six Z-Image Turbo quantization configs on DGX Spark GB10 — BF16 baseline, FP8 cast standard, FP8 cast fast, FP8 scaled (Kijai), NVFP4, NVFP4+FP8 encoder. With N=10 isolated GPU, NVFP4 transformer hits 5.50s warm versus BF16 7.55s (1.37× faster). All three FP8 paths are slower than BF16. Model working set drops from 20.6 GB (BF16) to 11.5 GB (NVFP4+FP8 encoder) — 44% smaller.

Does Z-Image Turbo quantization break image quality? Two-axis benchmark — LPIPS (perceptual distance vs BF16) + CLIPScore (image-text alignment) — across 6 prompts × 4 configs × 3 seeds = 72 samples. Result: NVFP4 produces images that look different from BF16, but no measured regression in this sample — all 4 configs land within ±0.04 std on CLIPScore, smaller than the noise floor. Production users should re-verify with their own prompt set.