Diffusion LLMs show more stability under extreme quantization compared to AR models
wFXx · reddit · 2026-08-03
An independent researcher published a comparative experiment analyzing the performance of diffusion language models versus autoregressive (AR) models under low-bit quantization.
Main Findings:
- 130M Parameters (INT4 PTQ): The diffusion model showed noticeably less degradation than the AR baseline across PTB, Wikitext-103, and LAMBADA.
- 7M Parameters (Ternary QAT): The diffusion model exhibited no extra performance tax, maintaining surprising parity with AR models.
Conducted on an RTX 2080 Super, the author has open-sourced all configs, evaluation outputs, and replication notes on GitHub, encouraging the community to scale the experiments on stronger hardware.
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