"Foundation models work. GPUs deliver. But somewhere between raw data and inference, enterprises hit a wall. GPUs sit underutilized because training pipelines can't move data fast enough. Real-time inference stalls waiting for distributed data to arrive. And when teams try to scale beyond a handful of use cases, data becomes the constraint that breaks everything. The Conversation That Needs to Happen"
"During GTC week, NVIDIA, Hammerspace, and The Register are hosting an off-the-record executive roundtable with senior infrastructure leaders actually operating AI at scale. This is peer-to-peer dialogue designed to surface what's actually breaking when scaling AI across hybrid and multi-cloud environments-and what an effective data platform looks like in practice. Attendees will discuss where AI initiatives break down, how enterprises are enabling real-time inference at scale, and what future-ready data platforms look like. Conducted under Chatham House Rules to encourage candid, unfiltered conversation."
Promo AI projects fail at scale not because models are ineffective or GPUs lack performance but because data pipelines cannot keep pace. Foundation models and GPUs function correctly, yet enterprises encounter a bottleneck between raw data and inference. Training pipelines cannot move data fast enough, producing GPU underutilization. Real-time inference stalls while waiting for distributed data, and scaling beyond a few use cases makes data the primary constraint. An off-the-record executive roundtable hosted by NVIDIA, Hammerspace, and The Register will gather senior infrastructure leaders to surface operational failures, strategies for enabling real-time inference at scale, and traits of future-ready data platforms under Chatham House Rules.
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