**面对大模型“大成本”挑战，如何提高算力效率成关注焦点**
中新网北京报道，随着大模型的火热发展，全球各大公司纷纷加大投入，寻求在人工智能领域的领先地位。然而，大规模模型的扩张背后，随之而来的是巨大的算力与能源成本挑战。
近期，Meta宣布追加投资10亿美元用于AI芯片研发和AI数据中心建设，亚马逊亦投入110亿美元建设新数据中心。尽管投入巨大，如何提高算力效率仍是行业关注的焦点。随着模型规模的增大，对算力的需求急剧上升，如何优化资源配置、降低能耗成为行业亟待解决的问题。
业内专家指出，当前应寻求在算法、硬件和软件三个层面的协同优化。在算法层面，探索更为高效的模型架构和训练策略；在硬件层面，发展专用AI芯片和加速器；软件层面则着重于系统优化和智能资源管理。此外，还需要加强跨领域合作，共同面对这一挑战。
面对大模型的“大成本”，全球各大科技公司正积极寻求解决方案，以提高算力效率为核心，共同推动人工智能的可持续发展。
英语如下：
News Title: The Computational Challenge and Countermeasures behind the Costly Expansion of Large Models
Keywords: Large Model Challenges, Computational Efficiency, Energy Cost
News Content:
**Focusing on Enhancing Computational Efficiency to Face the Costly Challenge of Large Models**
Beijing, China News Service: As the development of large models becomes increasingly popular, companies worldwide are intensifying their investments to seek leadership in the field of artificial intelligence. However, behind the expansion of these large-scale models comes a significant challenge of computational power and energy costs.
Recently, Meta announced an additional investment of $1 billion for AI chip research and development as well as AI datacenter construction, and Amazon has invested $11 billion in building new datacenters. Despite these enormous investments, enhancing computational efficiency remains a focal point for the industry. As model sizes grow, the demand for computational power increases exponentially, and optimization of resource allocation and reduction in energy consumption are pressing issues within the industry.
Industry experts point out that a协同 optimization approach should be taken at three levels: algorithm, hardware, and software. In terms of algorithm, exploring more efficient model structures and training strategies is essential. In hardware, dedicated AI chips and accelerators should be developed. Software efforts should focus on system optimization and intelligent resource management. Additionally, strengthened cross-field collaboration is needed to jointly face this challenge.
Facing the “big costs” of large models, major tech companies worldwide are actively seeking solutions, with the core focus on enhancing computational efficiency to jointly promote the sustainable development of artificial intelligence.
【来源】http://www.chinanews.com/cj/2024/06-03/10227860.shtml
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