Most of these backends are weight-only. This means that they store the weights in low precision and dequantize them back to high precision at compute time. This reduces memory usage significantly, but it usually does not make inference faster, and can even add a small latency overhead.

SVDQuant, the quantization method behind the popular Nunchaku inference engine, takes a different approach. It runs the main transformer layers with 4-bit weights and activations (W4A4), reducing memory while also speeding up the denoising loop. The details are covered below, but until now, using these checkpoints required a separate inference library.

With current Diffusers, loading a Nunchaku checkpoint is as simple as calling from_pretrained(), with no local CUDA compilation required thanks to the kernels package. In addition, the companion diffuse-compressor toolkit lets you quantize new architectures yourself and publish them as regular Diffusers repositories.

First, install the requirements. You need a recent version of Diffusers and the Hugging Face kernels package:

No custom pipeline class or separate inference engine is needed, and there is nothing to compile locally. The NVFP4 kernels are downloaded from the Hub through the Nunchaku Lite kernels page the first time they are used. 7 seconds on an RTX 5090 with a peak memory usage of about 12 GB, compared with about 24 GB for the BF16 pipeline. You can find more details about the Nunchaku Lite checkpoint format in the official Diffusers documentation .

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