Scaling AI Workloads with Decentralized GPU Networks: A Systems Perspective

As state-of-the-art AI models continue to scale, access to efficient, cost-effective compute has become a bottleneck for both industry and academic research. In this talk, we present a systems level analysis of decentralized GPU networks as a viable alternative to conventional centralized cloud infrastructure. We evaluate the performance characteristics of large scale training and inference workloads running on heterogeneous, globally distributed GPU clusters, focusing on throughput, latency, fault tolerance, and cost efficiency. We further discuss scheduling strategies, orchestration challenges, and reliability considerations unique to decentralized environments, along with empirical benchmarks comparing decentralized and centralized clusters across common ML workloads. Our goal is to highlight how decentralized compute can broaden accessibility for researchers, reduce costs for large scale experimentation, and offer new design spaces for distributed training in the era of rapidly growing model complexity.