一台GPU用了三年，为什么租金反而更贵了？
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Merged summary
TL;DR - Older GPUs such as Nvidia H100s can command higher rental prices years after deployment because AI compute value depends on cluster integration, utilization, and sustained demand—not chip age alone. This makes GPU cloud infrastructure a vertically integrated systems business rather than a standardized hardware commodity.
- Multi-GPU performance depends heavily on high-speed interconnects, network topology, storage, memory, and orchestration; identical GPU counts can therefore deliver different effective compute.
- Product-generation cycles, accounting depreciation, and revenue-producing economic life are distinct, allowing older GPUs to remain valuable as long as workloads and demand persist.
- Low utilization sharply raises unit costs because equipment continues depreciating while idle; at 50% utilization, each active GPU-hour bears roughly twice the depreciation cost.
- Scheduling software that jointly allocates compute, storage, and networking is crucial for converting large GPU clusters into reliable, on-demand training and inference capacity.
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一台GPU用了三年，为什么租金反而更贵了？
TL;DR - Older GPUs such as Nvidia H100s can command higher rental prices years after deployment because AI compute value depends on cluster integration, utilization, and sustained demand—not chip age alone. This makes GPU cloud infrastructure a vertically integrated systems business rather than a standardized hardware commodity.
- Multi-GPU performance depends heavily on high-speed interconnects, network topology, storage, memory, and orchestration; identical GPU counts can therefore deliver different effective compute.
- Product-generation cycles, accounting depreciation, and revenue-producing economic life are distinct, allowing older GPUs to remain valuable as long as workloads and demand persist.
- Low utilization sharply raises unit costs because equipment continues depreciating while idle; at 50% utilization, each active GPU-hour bears roughly twice the depreciation cost.
- Scheduling software that jointly allocates compute, storage, and networking is crucial for converting large GPU clusters into reliable, on-demand training and inference capacity.