Compute Arbitrage
Date of Addition
17 Oct 2025
An economic strategy that exploits geographic, temporal, or market-based pricing differentials in GPU compute resources to reduce costs for AI model training and inference workloads. This practice involves dynamically shifting computational tasks across cloud regions with lower electricity rates, opportunistically utilizing spot instances during off-peak hours, or leveraging jurisdictional variations in data center operational expenses. Compute arbitrage has emerged as a critical cost optimization discipline for AI companies as training expenses for frontier models reach tens of millions of dollars, with sophisticated operators achieving 30-50% cost reductions through strategic resource allocation across heterogeneous infrastructure providers.
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