Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based token generation.
However, their growing model sizes and high inference costs make efficient deployment challenging: full-sequence denoising repeatedly invokes compute-intensive forward passes, while block-diffusion models additionally introduce a memory-intensive KV-cache.
Low-bit weight-activation quantization is therefore attractive, yet existing dLLM post-training quantization methods rely on calibration data despite activation distributions shifting across masking states and denoising steps.
We present SoloQ, a calibration-free quantization framework that maps weights and activations into a normalized rotated basis with a predictable marginal distribution, enabling data-independent quantization.
SoloQ combines a structured K-RPBH rotation with a lightweight rescaling correction for calibration-free quantization.
Its predictable post-rotation distribution supports both distribution-matched codebooks and hardware-native NVFP4.
For block-diffusion models, SoloQ further applies commit-time KV-cache quantization to compress persistent states without perturbing the actively denoised block.
Across full-sequence dLLMs (LLaDA and Dream) and block-diffusion dLLMs (Fast-dLLM v2 and Nemotron-Labs-Diffusion), SoloQ retains accuracy under 4-bit quantization and outperforms calibration-based baselines on knowledge- and reasoning-intensive benchmarks.
With NVFP4, SoloQ reduces peak memory by up to 2.61X and accelerates end-to-end inference by up to 2.24X.