The paper introduces Conditional Program (CP), a compressed representation for large language model memory tables that reconstructs only the rows requested by an input. CP encodes each row with a short two‑bit code and a local scale, using a shared reconstruction program based on a Gaussian trellis. Experiments on three n‑gram memory families show CP reduces output KL divergence by 41–63% compared to 2‑bit round‑to‑nearest, and for DeepSeek it cuts table storage from 203 GB to 51 GB, enabling larger workloads on GPU.
By Donghoon Han, Minseop Jung, Dong Woo Kwak, SeungJae Lee, Junhoo Lee
Conditional memory expands language-model capacity through selective lookup, but the full embedding table must remain available for every possible input. We introduce Conditional Program (CP), a compr...
The paper introduces COUNT, a method that builds n‑gram embeddings without adding learned parameters by storing precomputed n‑gram counts on host memory. By weighting the language model’s own unembeddings with corpus frequencies, COUNT can be added as an extra input embedding during pretraining, improving validation loss equivalent to a 57% increase in compute for a 1B‑parameter model. Additionally, COUNT enables efficient GPU memory usage and allows domain‑specific n‑gram tables to be swapped in at inference time for training‑free adaptation.
By Ian Magnusson, Noah A. Smith, Pang Wei Koh
arXiv:2610.22428v1 Announce Type: new
Abstract: In a small language model the vocabulary is the single largest part. In Gemma 3 270M the embedding table holds 168M of 268M parameters, and because qua...
By Sai Kiran Bathula
arXiv:2605. 08692v2 Announce Type: replace Abstract: Post-training weight-only quantization to 4 bits is widely used to reduce the memory and compute costs of large language model inference.
By Beshr IslamBouli, David Jin
arXiv:2610.27699v1 Announce Type: new
Abstract: Low-bit quantization reduces the memory footprint of large language models, but preserving accuracy with efficient training remains challenging. Scalar...
By Xinyu Lei, Hong Huang, Jiebin Zhang, Hongyu Li, Jinlong Song, Decheng Wu, Guanghua Yu, Jianchen Zhu, Sujian Li
arXiv:2607. 07388v1 Announce Type: cross Abstract: Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion costly through pretraining, fine-tuning, retrieval augmentation, or longer contexts.
By Yutang Ma, Kecheng Huang, Xikun Jiang, Zili Shao
arXiv:2507.17417v4 Announce Type: replace
Abstract: For large language models (LLMs), post-training quantization (PTQ) significantly reduces memory and computational overhead. Despite rapid advanceme...
By Yutong Liu, Cairong Zhao, Guosheng Hu
arXiv:2608. 15602v1 Announce Type: cross Abstract: While binary quantization theoretically promises extreme compression and acceleration for Large Language Models (LLMs), existing research often overlooks the necessity of specialized hardware kernels, thus failing to unleash the full acceleration potential due to persistent reliance on expensive floating-point arithmetic or runtime dequantization overheads.
By Qingyao Yang, Runming Yang, He Xiao, Wendong Xu, Junyu Chen, Haobo Liu, Chenchen Ding, Ruihan Hu, Yik-Chung Wu, Ngai Wong
arXiv:2607. 16339v1 Announce Type: new Abstract: Diffusion-based Large Language Models(DLLMs) enable parallel generation via Semi-Autoregressive (SAR) decoding in text generation.
By Xingru Chen, Zelang Liang, Yongjia Ma, Jiqing Zhan, Shuling Yang, Lian Wen, Kun Zhan
arXiv:2605.19660v3 Announce Type: replace
Abstract: The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a domina...
By Zunhai Su, Rui Yang, Chao Zhang, Yaxiu Liu, Yifan Zhang, Wei Wu, Jing Xiong, Dayou Du, Xialie Zhuang, Yulei Qian, Jianchao Tan, Yuchen Xie, Yik-Chung Wu, Hongxia Yang, Ngai Wong
The paper "LLM Inference in a Flash!" proposes an integer‑only quantization scheme and a dictionary‑based KV cache compression technique to enable large language model inference on compute‑in‑flash (CIF) devices. By eliminating floating‑point operations and reducing KV cache traffic through sparse dictionary coding, the authors achieve minimal accuracy loss while cutting dynamic KV cache traffic by 15× on Llama‑3.1‑8B and Qwen‑2.5‑7B models.
By Sebastian Zhao, Minseo Kim, Coleman Hooper, Luca Manolache, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami