---
id: 20260424-T0-12
title: "TTKV：解决长上下文LLM推理内存瓶颈的新方案"
title_en: "TTKV: Solving Long-Context LLM Memory Bottleneck"
url: https://ai.daily.yangsir.net/daily/20260424-T0-12
issue_date: 2026-04-24
publish_date: 2026-04-23T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2604.19769
---

# TTKV：解决长上下文LLM推理内存瓶颈的新方案

研究者提出TTKV（Temporal-Tiered KV Cache）方法，解决长上下文LLM推理中的内存线性增长问题。该方案通过时间分层缓存技术，将KV内存占用从线性优化为对数级别，大幅提升长文档处理效率。实验显示在保持性能的同时，内存使用减少60%以上，为长上下文模型部署提供新思路。

## English Version

**TTKV: Solving Long-Context LLM Memory Bottleneck**

Researchers propose TTKV (Temporal-Tiered KV Cache) to solve linear memory growth in long-context LLM inference. This time-tiered caching technique reduces KV memory usage from linear to logarithmic scale, significantly improving long-document processing efficiency. Tests show 60%+ memory reduction while maintaining performance.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2604.19769)

**详情页**：https://ai.daily.yangsir.net/daily/20260424-T0-12

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