GPU寿命，远超想象

Core Viewpoint - The prevailing concern regarding the depreciation of GPUs in the AI industry is largely unfounded, as the actual depreciation cycle is more favorable than many investors believe [1][2]. GPU Depreciation and Lifespan - Analysts suggest that the profit cycle for GPUs is approximately 6 years, and the depreciation accounting practices of major cloud computing firms are deemed reasonable [2]. - The cost of operating GPUs in AI data centers is significantly lower compared to the GPU rental market, allowing for a high marginal contribution rate when extending the lifespan of older GPUs [3]. - GPUs can have a practical lifespan of 7 to 8 years, with many companies still using GPUs that are over 5 years old and generating substantial profits [5]. Lifecycle Transition of GPUs - GPUs transition from high-performance tasks, such as training advanced AI models, to lower-demand inference workloads, allowing older GPUs to remain in active service [6]. - The variety of AI workloads enables older GPUs to be repurposed effectively, maintaining their profitability [6]. Cost Considerations - AI cloud computing companies often choose GPUs based on user expectations and budget, with older GPUs being utilized for lower-tier services while newer models are reserved for premium offerings [7]. - Many AI services can run on open-source models that require less computational power, further enhancing the utility of older GPUs [8]. Economic Advantages of Older GPUs - Despite higher energy consumption, older GPUs are often preferred due to their lower procurement costs, making them more cost-effective overall [10].