Gpu utilization

The AI Frontier: from FLOPs to Megawatts — Anjney Midha, AMP

The AI Frontier: from FLOPs to Megawatts — Anjney Midha, AMP

Anjney Midha unpacks the critical bottlenecks in AI scaling beyond just GPU acquisition, advocating for responsible infrastructure, community-aligned data centers, and an independent system operator model for compute. He discusses the perils of research hoarding, the rise of researcher CEOs, and how Anthropic's culture of "preparedness" and "output maxing" led to its success, while also highlighting his personal mission to use AI for precise end-of-life prediction.

Inside the $41B AI Cloud Challenging Big Tech | CoreWeave SVP

Inside the $41B AI Cloud Challenging Big Tech | CoreWeave SVP

Corey Sanders, SVP of Product at CoreWeave, explains why the unique, high-stakes demands of AI workloads are driving a shift away from general-purpose clouds toward specialized "Neo Clouds." He details the specific hardware and software innovations in storage, cooling, and networking that allow CoreWeave to maximize GPU utilization and deliver superior performance, arguing that this focused approach creates a durable competitive advantage.

Efficient Reinforcement Learning – Rhythm Garg & Linden Li, Applied Compute

Efficient Reinforcement Learning – Rhythm Garg & Linden Li, Applied Compute

At Applied Compute, efficient Reinforcement Learning is critical for delivering business value. This talk explores the transition from inefficient synchronous RL to a high-throughput asynchronous 'Pipeline RL' system. The core challenge is managing 'staleness'—a side effect of in-flight weight updates that can destabilize training. The speakers detail their first-principles systems model, based on the Roofline model, used to simulate and find the optimal allocation of GPU resources between sampling and training, balancing throughput with algorithmic stability and achieving significant speedups.