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...
arXiv:2610.03027v1 Announce Type: cross Abstract: The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwid...
arXiv:2607. 01065v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) with extended context windows is increasingly constrained by the linear growth of Key-Value (KV) cache memory.
arXiv:2608.28911v1 Announce Type: new Abstract: The key-value (KV) cache is the dominant memory bottleneck of long-context large language model (LLM) inference, growing linearly with context length....
PN-Mix is a mixed‑precision key‑cache quantization technique for RoPE‑based decoder‑only large language models that allocates higher precision to salient channel pairs identified during the prefill stage. By focusing on channel pairs with large query–key pair‑norm products, PN‑Mix reduces logit variation caused by quantization errors. Experiments on reasoning, code‑generation, and AIME benchmarks show that PN‑Mix achieves comparable or better accuracy than prior methods while using an average precision of 2.26 bits and degrades gracefully down to 1.5 bits.
arXiv:2608. 04074v1 Announce Type: cross Abstract: Long-context LLM decoding reads the key-value (KV) cache at every step.
MultiAlloc is a profiling‑guided framework that allocates limited quantization resources between layer‑wise precision enhancement and token‑wise preservation for KV cache quantization in large language models. By using lightweight offline profiling to assess layer sensitivity and token importance, it automatically derives an effective precision allocation strategy that satisfies a fixed memory budget without online search. Experiments on reasoning and long‑context benchmarks show that MultiAlloc consistently outperforms existing layer‑wise, token‑wise, and multi‑granularity quantization methods across models such as Mistral, Llama, DeepSeek‑Qwen, and QwQ.
arXiv:2608. 14191v1 Announce Type: new Abstract: The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference.
The paper introduces JoLT, a training‑free compressor that jointly allocates rank and precision for key‑value (KV) cache compression in long‑context language models. JoLT treats grouped prefill caches as fourth‑order tensors, applies partial Tucker decomposition along token and feature modes, and uses a rotated low‑bit quantizer for residuals, all governed by a single Lagrangian dual under a global byte constraint. Across five models from four architecture families, JoLT achieves 2–3× compression with less than 0.2% perplexity loss, and near‑lossless retrieval accuracy on LLaMA‑3.1‑8B at 64K context up to 3× compression.
Dual-QK introduces paired non‑orthogonal transforms for queries and keys to enable sharp queries and flat keys, improving 2‑bit quantization and dynamic channel pruning of KV caches. The method balances key scales for INT2 quantization while concentrating query energy for pruning, and supports channel‑0 protection and bucket‑relative RoPE for long‑context accuracy. Experiments on four models across five generative benchmarks show that Dual‑QK outperforms OSCAR at 40% query‑channel sparsity, achieving 6.8× KV‑cache compression, an 8.3× reduction in KV read volume, and up to 3.75× decoding throughput over unpruned BF16.
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.
The paper introduces D-Quant, a KV cache quantization framework that addresses the memory bottleneck of large language models by using a drift mechanism to convert entropy-coded representations into fixed-size bitstreams. This approach leverages the non-uniform distribution of KV cache values—after rotation and normalization, they approximate a normal distribution—allowing entropy coding to assign shorter codewords to frequent symbols while maintaining regular memory layouts suitable for parallel attention kernels. D-Quant thus aims to reduce memory footprint and bandwidth usage without sacrificing performance.