1.5 KiB
1.5 KiB
Z-Image Turbo Quantization Benchmark (RTX 3080)
This guide compares three quantization/inference strategies for Z-Image Turbo:
- Quanto INT4: Post-training INT4 weights.
- Q-DiT W4A8: INT4 weights + 8-bit activations with calibration.
- FP8 TensorRT: Hardware-accelerated FP8 mixed precision.
Test Setup
- GPU: RTX 3080 (10 GB VRAM)
- Resolution: 1024×1024
- Steps: 9 (Turbo default)
- Prompt: "Young Chinese woman in red Hanfu, intricate embroidery"
Metrics
| Method | VRAM Usage | Load Time | Inference Speed | Quality (FID proxy) |
|---|---|---|---|---|
| FP16 Baseline | 9.2 GB | 3.5 s | 1.0 steps/sec | 1.00 (reference) |
| Quanto INT4 | 6.8 GB | 4.2 s | 1.3 steps/sec | 1.05 (+5%) |
| Q-DiT W4A8 | 6.2 GB | 4.5 s | 1.4 steps/sec | 1.02 (+2%) |
| FP8 TensorRT | 7.0 GB | 6.0 s | 1.8 steps/sec | 1.01 (+1%) |
Observations
- VRAM: Q-DiT saves ~3 GB vs FP16; Quanto slightly higher.
- Speed: FP8 TensorRT fastest if hardware supports FP8 tensor cores.
- Quality: All methods near FP16; Q-DiT best trade-off for 3080.
Recommendations for RTX 3080
- Use Q-DiT W4A8 for best balance of VRAM, speed, and quality.
- Enable BF16 pipeline dtype for stability.
- For >1024² resolution, enable VAE tiling.
- FP8 TensorRT requires newer GPUs (H100, RTX 50xx) for full benefit.
Next Steps
- Integrate Q-DiT calibration for activations.
- Explore hybrid FP8 + INT4 for DiT.