arXiv:2608. 07019v1 Announce Type: new Abstract: Post-training quantization (PTQ) is widely used to reduce the memory and computational cost of large language models.
By Yongge Ma, Guoan Wang, Feiyu Wang, Yaoming Li, Qian Zhang, Zihan Yan, Yinjun Han, Tong Yang
Q-PACE is a dynamic precision allocation method for quantization-aware training that uses a second-order sensitivity model to predict loss increases from quantization noise. By periodically recalculating per-layer curvature coefficients and reassigning precision, it improves mixed-precision training for large language models up to 4 B parameters, achieving comparable loss with lower memory usage. The approach shows that quantization sensitivity is predictable by depth and layer type, enabling infrequent, inexpensive recalibration.
By Alexandra Volkova, Matin Ansaripour, Erik Schultheis, Christoph H. Lampert, Dan Alistarh
arXiv:2607. 23047v1 Announce Type: cross Abstract: Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing methods solve the allocation for a single fixed memory budget.
By Ashitabh Misra, Madhav Agrawal, Arham Jain, Tarek Abdelzaher
arXiv:2607. 08241v1 Announce Type: cross Abstract: Deploying classifier-free guidance (CFG) diffusion models under real-world compute budgets requires quantization, yet existing post-training quantization (PTQ) methods treat CFG models as single-branch networks, ignoring the paired conditional/unconditional structure that CFG inference fundamentally relies on.
By Abdullah Al Shafi, Sumaiya Rahim Suma
The paper presents a target‑centric survey of Quantization‑Aware Training (QAT), a technique that simulates quantization during model training to produce low‑bit models with accuracy comparable to full‑precision ones. It systematically reviews existing QAT methods using a target‑centric taxonomy, highlighting differences in error characteristics, numerical formats, and strategy transferability across targets. The survey also summarizes QAT evaluation paradigms, discusses optimization and deployment challenges, and outlines potential future research directions.
By Jiamin Song, Mengjie Zhao, Zijing Wang, Yongkang Liu, Qian Li, Shi Feng, Feiliang Ren, Daling Wang, Hinrich Sch\"utze
arXiv:2605. 17160v2 Announce Type: replace-cross Abstract: Model quantization is widely used to reduce memory, latency, and deployment cost, and is typically judged by whether predictive accuracy is preserved.
By Chaymae Yahyati, Ismail Lamaakal, Khalid El Makkaoui, Ibrahim Ouahbi