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Speed:

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