E8 Lattice Key Quantization for Low-Bit KV-Cache Compression
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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.