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AI Cloud Horizons

Expert insights on the future of GPU Infrastructure

From benchmark results to real-world TCO, get a
front-row seat to the conversations shaping enterprise AI infrastructure.

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Episode 1
Live

Burn-In to Breakthrough: Insights from Operating Large Scale AI Clusters

What makes a GPU cloud truly enterprise-ready? Join CoreWeave CTO Peter Salanki in conversation with Dylan Patel, the lead analyst behind the groundbreaking SemiAnalysis ClusterMAX™ report.

In this podcast, they break down what sets GPU cloud providers apart—and what it means for enterprises training and deploying the next wave of AI models. They also talk about the recent NVIDIA GB200 deployments, the challenges of scaling,
and more.

Burn-In to Breakthrough: Insights from Operating Large Scale AI Clusters
Episode 2
AIRS JULY 29

The SLA is Not Enough: Redefining Reliability for AI Infrastructure

SLAs look good on paper, but when a single GPU node goes down mid-training, what happens next?

In this session, we’ll explore why traditional SLAs fail to meet the needs of distributed AI workloads—and how modern GPU clouds are redefining what reliability really means. Learn how CoreWeave treats the SLA as a living operational framework, not just a legal checkbox.

Episode 3
AIRS AUGUST 26

Beyond $/GPU-Hour: How to Really Measure TCO for AI Cloud

How do enterprises calculate the total cost of ownership (TCO) for GPU Clouds?

Join Urvashi Chowdhary, Head of Product at CoreWeave, for a deep dive into the real drivers of GPU cloud TCO. We’ll go beyond surface-level pricing to explore the technical and operational decisions that shape cost, efficiency, and performance.

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