关于高效大型语言模型的调查:原则,算法,应用程序和开放问题
IEEE transactions on neural networks and learning systems
|November 14, 2025
概括
本调查探讨了大型语言模型 (LLM) 推断加速技术,详细介绍了量子化和修剪等方法,以提高可扩展的LLM系统的效率并降低可扩展的LLM系统的计算成本.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 大型语言模型 (LLM) 面临着计算和部署的挑战,因为它们的规模和复杂性越来越大.
- 推理优化技术对于加速LLM性能,同时保持准确性至关重要.
研究的目的:
- 为LLM推断加速策略提供全面的调查.
- 从基础,算法和基于应用的角度分析这些技术.
- 为研究人员和从业人员提供可扩展和高效的LLM系统的见解.
主要方法:
- 将现有的LLM推理优化技术分类为一个新的分类法.
- 分析方法包括量化,修剪,蒸,高效架构,编译和硬件意识的方法.
- 检查整个LLM生命周期 (培训,微调,服务) 中的技术相互作用.
主要成果:
- 对各种LLM推断加速策略的结构化概述.
- 确定有效的LLM开发中的关键应用和新兴趋势.
- 讨论开放式研究的挑战和部署的实际考虑.
结论:
- 优化LLM推断对于解决计算成本和部署障碍至关重要.
- 有效的LLM系统需要系统地理解和应用这些技术.
- 需要进一步的研究来解决该领域的新兴趋势和未解决的问题.
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