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AI data center spending is projected to exceed $1 trillion in the coming years. But with the current architecture, are the economics of large-scale AI sustainable? Today’s massive AI models can’t fit into the memory of a single GPU or accelerator, forcing them to be split across multiple units. While this boosts memory capacity and bandwidth, it also introduces a critical tradeoff — waiting for different GPUs to sync before computation can even begin.
The biggest bottlenecks in AI systems — compute, memory, and interconnect — are holding back both performance and profitability. How can we address these pain points, minimize communication delays, and create hardware architectures that are both high performing and cost efficient?
This panel discussion will explore cutting-edge solutions designed to break through the limits of today’s infrastructure, maximizing throughput and interactivity while reducing energy consumption. Join moderator Timothy Prickett Morgan from The Next Platform along with industry experts to discover the strategies that will shape the future of AI hardware, turning massive investments into lasting profitability.
To find out more about Ayar Labs, click here.
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