GPU Clouds, Aggregators, and the New Economics of AI Compute

About this episode

Summary 
In this episode I sit down with Hugo Shi, co-founder and CTO of Saturn Cloud, to map the strategic realities of sourcing and operating GPUs across clouds. Hugo breaks down today’s provider landscape—from hyperscalers to full-service GPU clouds, bare metal/concierge providers, and emerging GPU aggregators—and how to choose among them based on security posture, managed services, and cost. We explore practical layers of capability (compute, orchestration with Kubernetes/Slurm, storage, networking, and managed services), the trade-offs of portability on “Kubernetes-native” stacks, and the persistent challenge of data gravity. We also discuss current supply dynamics, the growing availability of on-demand capacity as newer chips roll out, and how AMD’s ecosystem is maturing as real competition to NVIDIA. Hugo shares patterns for separating training and inference across providers, why traditional ML is far from dead, and how usage varies wildly across domains like biotech. We close with predictions on consolidation, full‑stack experiences from GPU clouds, financial->
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• Your host is Tobias Macey and today I'm interviewing Hugo Shi about the strategic realities of sourcing GPUs in the cloud for your training and inference workloads

Interview

• Introduction
• How did you get involved in machine learning?
• Can you start by giving a summary of your understanding of the current market for "cloud" GPUs?
• How would you characterize the customer base for the "neocloud" providers?
• How is the access to the GPU compute typically mediated?
• The predomina