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 investigates reducing memory usage of n‑gram embedding tables in open‑weight language models by storing them in low precision. Experiments on nanochat, Llama, and GPT‑2 families show that four‑bit tables cut table memory by about 75% while retaining over 99% of the validation‑loss improvement. The study also demonstrates that re‑allocating the saved memory to larger tables can surpass the performance of full‑precision tables, and that similar savings are achievable in large models like DeepSeek‑V4.1‑Flash and Qwen3.8‑Flash‑Next with minimal impact on perplexity and downstream tasks.
By Stephen Chung, Wenyu Du
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:2607. 07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantization, eviction, or offloading.
By Vladimir Gusev
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: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