SoloQ quantizes diffusion language models to 4 bits without calibration, holding accuracy on LLaDA, Dream, Fast-dLLM v2 and Nemotron-Labs-Diffusion while beating calibration-based baselines
The authors present SoloQ, which uses a K-RPBH rotation to map weights and activations into a predictable near-Gaussian basis plus a scale-only bias correction for calibration-free 4-bit quantization, and quantizes the KV cache at block commit for block-diffusion models; across LLaDA-Base, LLaDA-1.5, Dream-7B, Fast-dLLM v2 and Nemotron-Labs-Diffusion, SoloQ-C and SoloQ-N retain accuracy under W4A4 and W4A4KV4 and exceed calibration-based baselines such as DLLMQuant++, STaR-Quant and FAIR-Calib on average, with the NVFP4 path also cutting peak memory and speeding up end-to-end inference.