arXiv:2608. 11240v1 Announce Type: new Abstract: Vector quantization is an old problem but has recently become central to AI infrastructure.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606. 00289v1 Announce Type: new Abstract: Quantization is a fundamental tool used to compress datasets, neural network weights, and memory usage in a range of computational tasks.
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.
arXiv:2606. 10531v1 Announce Type: cross Abstract: Quantization-aware training (QAT) is essential for extremely low-bit large language models (LLMs).
arXiv:2505. 18231v3 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference is typically memory-intensive, especially when processing large batch sizes and long sequences, due to the large size of key-value (KV) cache.