---
id: 20260317-T0-16
title: "ActTail实现LLM全局激活稀疏化加速推理"
title_en: "ActTail Achieves Global Activation Sparsity for LLM Speedup"
url: https://ai.daily.yangsir.net/daily/20260317-T0-16
issue_date: 2026-03-17
publish_date: 2026-03-16T04:00:00.000Z
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2603.12272
---

# ActTail实现LLM全局激活稀疏化加速推理

arXiv论文提出ActTail方法，实现大型语言模型的全局激活稀疏化。与现有方法不同，ActTail能根据输入动态调整不同投影层的稀疏模式，在保持98%准确率的情况下，将推理速度提升2.3倍，内存占用减少40%。该方法已在7B和13B参数模型上验证有效。

## English Version

**ActTail Achieves Global Activation Sparsity for LLM Speedup**

arXiv paper proposes ActTail method for global activation sparsity in LLMs. Dynamically adjusts sparsity patterns per layer based on input, achieving 2.3x speedup and 40% memory reduction while maintaining 98% accuracy. Validated on 7B and 13B parameter models.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2603.12272)

**详情页**：https://ai.daily.yangsir.net/daily/20260317-T0-16

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