The paper presents TTS‑Q, a post‑training quantization method that reduces the size of large diffusion transformer TTS models to 4‑bit precision while preserving speaker timbre and intelligibility. By identifying hard‑to‑quantize weights and activations and applying a mixed‑precision schedule that gives higher precision to late‑step feed‑forward activations, TTS‑Q restores timbre loss observed in standard 4‑bit PTQ. The approach also offers a calibrated‑W4A4 reference that lowers GPU memory usage, making TTS diffusion transformers more viable for on‑device, assistive, and real‑time applications.
By Xiaoqin Feng, Yuqi Li, Weilun Feng, Yi-Cheng Lin, Kai Li, Xianglong Wang, Kuo Yang, Chuanguang Yang, Yingli Tian, Wei Li, Zhongwei Cheng, Lin Chen
The paper evaluates post‑training quantization (PTQ) for text‑to‑speech (TTS) models across multiple architectures using a unified protocol. It shows that reducing weights to 4‑bit per‑channel can significantly lower predicted mean opinion scores (UTMOS) and that even 8‑bit per‑tensor scaling can cause severe degradation, with the impact varying by model. A staged ablation identifies the sensitive components, and per‑layer GPTQ can recover performance to within 0.1 UTMOS, while real int8 and int4 kernels confirm the simulated results on hardware, demonstrating that each configuration must be validated on the target runtime.
By Se Un Park, Yutae Kim, Junyoung Park
Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment. Ternarization has emerged as a promising compression technique, offering significant reductions in model size and inference complexity.
The paper presents a new evaluation protocol for post‑training quantization of speech language models that separates lexical output, transcript‑insufficient endpoints, and packed implementations. In a Qwen2‑Audio case study, a 6‑bit allocation selected for translation improves chrF scores but degrades emotion recognition, while uniform and front‑layer controls perform better on emotion tasks. Similar patterns hold at 7 bits, and a 4‑bit study shows consistent emotion deficits across all low‑bit allocations, with no advantage for the selected scheme. The study highlights a precision‑dependent mismatch between lexical output, waveform‑dependent behavior, and nominal precision, without claiming a general failure of low‑bit models or a deployment benefit for the selected allocation.
By Mengzhe Geng, Jinxi Jin, Junhao Xu
Squeeze10-LLM is a staged mixed‑precision post‑training quantization framework that reduces 16‑bit LLM weights to an average of 1.6 bits per weight by assigning 80% of weights to 1 bit and 20% to 4 bits. It introduces Post‑Binarization Activation Robustness (PBAR), a weight significance metric that considers activation impact, and Full Information Activation Supervision (FIAS), a strategy that preserves activation information to limit error propagation. Experiments on LLaMA and LLaMA2 demonstrate that Squeeze10‑LLM achieves state‑of‑the‑art performance for sub‑2‑bit weight‑only quantization, raising average accuracy from 43% to 56% on six zero‑shot classification tasks.
By Qingcheng Zhu, Yangyang Ren, Linlin Yang, Yanjing Li, Sheng Xu, Haodong Zhu, Juan Zhang, Runqi Wang, Baochang Zhang
arXiv:2606. 13054v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment.
By Zhixiong Zhao, Zukang Xu, Zhixuan Chen, Xing Hu, Zhe Jiang, Dawei Yang