---
license: gpl-3.0
tags:
  - kv-cache
  - llm-inference
  - optimization
  - compression
  - mla
  - deepseek
language:
  - zh
  - en
---

# 📄 KV Cache Compression for Text-Only LLMs

**纯文本大语言模型 KV 缓存压缩 — 完整技术报告** | **Full Technical Reports**

## Contents

| File | Description |
|------|-------------|
| `mla_absorbed_cache_report.md` | 🚀 MLA 吸收式缓存优化（中文） |
| `mla_absorbed_cache_report_en.md` | 🚀 **Absorbed MLA Cache Optimization (English)** |
| `kv_cache_compression_report.md` | 📊 KV 缓存压缩完整方案（中文） |
| `kv_compress_plan.md` | 🗺 详细实施计划（中文） |

## 🚀 Highlight: Absorbed MLA Cache (270KB → 8.4KB/token, 32×)

DeepSeek-V2-Lite MLA 优化，在 L40S 实测：

| Approach | KV/token | Compression | Error |
|----------|----------|-------------|-------|
| Standard MHA | 270 KB | 1x | — |
| Absorbed MLA | 30.4 KB | 8.9x | 0 |
| + per-channel INT8 | 15.2 KB | 17.8x | 0.011 |
| + INT4 (extreme) | **8.4 KB** | **32x** | 0.112 |

**Single L40S: 1.24M tokens context.**

## Key Insight

> Text-only models can be aggressively compressed (quantization + eviction), while reasoning models (R1-class) must be conservative (quantization + sliding window + tiered storage). **Scenario classification matters more than the algorithm itself.**

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*Cloud LTE Studio · 2026-08-08 · GPL-3.0 License*