综述大模型高效训练与推理技术，助其更省资源、更易用。
Designing Large Foundation Models for Efficient Training and Inference: A Survey
- 从模型与系统双角度优化大模型训练推理效率
- 通过技术改进降低计算开销，提升可用性
- 适合关注大模型落地与性能优化的研究者
本文聚焦于基础模型的高效训练与推理技术，从模型设计与系统设计两个视角进行阐述。模型与系统设计分别从不同层面优化大模型的训练与推理过程，以减少计算资源消耗，使大模型更加高效、经济且易于访问。相关代码与资料仓库见 https://github.com/NoakLiu/Efficient-Foundation-Models-Survey。
原文摘要 · Abstract (English)
This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and System Design optimize LLM training and inference from different aspects to save computational resources, making LLMs more efficient, affordable, and more accessible. The paper list repository is available at https://github.com/NoakLiu/Efficient-Foundation-Models-Survey.
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