Speed:
June 8, 2026 · View on GitHub
Measured on a 3090 at 1024x1024, 26 steps with Flux2 Klein Base 9B.
| Format | Speed (s/it) ↓ | Relative Speedup |
|---|---|---|
| bf16 | 2.07 | 1.00× |
| bf16 compile | 2.24 | 0.92× |
| fp8 | 2.06 | 1.00× |
| int8 | 1.64 | 1.26× |
| int8 compile ★ | 1.04 | 1.99× |
| gguf8_0 compile | 2.03 | 1.02× |
3090, Qwen Image 2512.
| Format | Speed (s/it) ↓ |
|---|---|
| Nunchaku INT4 Best Quality | 1.21 |
| Nunchaku INT4 with R128 Lora | 1.36 |
| INT8 ConvRot compile | 1.26 |
| INT8 Row compile ★ | 1.18 |
| INT8 R128 Lora | No slowdown, except if dynamic. |
I would also like to point out that we beat Nunchaku INT4 on every quality measurement in the Quality Metrics
Additionally, the quality of loras applied with this nunchaku lora node appears to be degraded.
Klein 9B, Measured on an 8gb 5060, same settings as the 3090 run:
| Format | Speed (s/it) ↓ | Relative Speedup |
|---|---|---|
| fp8 | 3.04 | 1.00× |
| fp8 fast | 3.00 | 1.00× |
| fp8 compile | couldn't get to work | ??× |
| int8 | 2.53 | 1.20× |
| int8 compile ★ | 2.25 | 1.35× |
8gb RTX 5060, Anima, Comfy version from 2026-05-02, Pytorch 2.11+CU13.0, latest kitchen triton and everything else
| Format | Speed (it/s) ↑ |
|---|---|
| bf16 | 0.78 |
| INT8 ConvRot | 1.12 |
| INT8 Row | 1.24 |
| INT8 ConvRot Compile | 1.47 |
| MXFP8 | 0.89 |
| MXFP8 --fast | 0.93 |
| MXFP8 + Compile | Still failing. |
Finally have gotten compile with --fast to work with mxfp8, PyTorch 2.13.0.dev20260511+cu132, RTX5060 same as before.
Quality results for this run, can be found here: Anima Results
| Format | Speed (it/s) ↑ |
|---|---|
| MXFP8 --fast + Compile | 1.37it |
| INT8 ConvRot + Compile | 1.47it |