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