Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs

Understand

Recent advances in diffusion large language models (dLLMs) have introduced a promising alternative to autoregressive (AR) LLMs for natural language generation tasks, leveraging full attention and denoising-based decoding strategies.

• However, the deployment of these models on edge devices remains challenging due to their massive parameter scale and high resource demands.
• While post-training quantization (PTQ) has emerged as a widely adopted technique for compressing AR LLMs, its applicability to dLLMs remains largely unexplored.
• In this work, we present the first systematic study on quantizing diffusion-based language models.