This August 2025 academic paper explores the application of post-training quantization (PTQ) to diffusion large language models (dLLMs), a promising alternative to traditional autoregressive LLMs for natural language generation. The authors conduct a systematic study to understand how existing PTQ techniques, commonly used for compressing AR LLMs, perform with dLLMs. A key finding is the prevalence of activation outliers in dLLMs, which pose a significant challenge for low-bit quantization. The research also evaluates the effectiveness of various quantization methods, bit-widths, task types, and model variants, concluding that 4-bit quantization is optimal for weight-only methods like GPTQ, while 8-bit is tolerable for weight-activation quantization, with rotation-based methods like DuQuant showing superior performance. The study ultimately aims to facilitate the efficient deployment of dLLMs on resource-constrained devices by providing practical insights into their quantization behavior.
Source:
https://arxiv.org/pdf/2508.14896
