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Nomads at the Summit: AI Models and their Corresponding Infrastructure Needs

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Manage episode 508482945 series 3492717
Content provided by Endeavor Business Media. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Endeavor Business Media or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

In this DCF Trends-Nomads at the Summit Podcast episode, Chris James, CEO of NoesisAI, delivers a sweeping, insight-rich overview of how different classes of AI models—from LLMs and RAG to vision AI and scientific workloads—are driving a new wave of infrastructure decisions across the data center landscape. With a sharp focus on the diverging needs of training vs. inference, James breaks down what it takes to support today’s AI—from GPU-intensive clusters with high-speed interconnects and liquid cooling to inference-optimized, edge-deployed accelerators. He also explores the rapidly shifting hardware ecosystem, including the rise of custom silicon, heterogeneous computing, and where the battle between NVIDIA, AMD, Intel, and hyperscaler-designed chips is headed. Whether you're designing for scalability, sustainability, or the bleeding edge, this conversation offers a field guide to the infrastructure behind intelligent computing.

  continue reading

157 episodes

Artwork
iconShare
 
Manage episode 508482945 series 3492717
Content provided by Endeavor Business Media. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Endeavor Business Media or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

In this DCF Trends-Nomads at the Summit Podcast episode, Chris James, CEO of NoesisAI, delivers a sweeping, insight-rich overview of how different classes of AI models—from LLMs and RAG to vision AI and scientific workloads—are driving a new wave of infrastructure decisions across the data center landscape. With a sharp focus on the diverging needs of training vs. inference, James breaks down what it takes to support today’s AI—from GPU-intensive clusters with high-speed interconnects and liquid cooling to inference-optimized, edge-deployed accelerators. He also explores the rapidly shifting hardware ecosystem, including the rise of custom silicon, heterogeneous computing, and where the battle between NVIDIA, AMD, Intel, and hyperscaler-designed chips is headed. Whether you're designing for scalability, sustainability, or the bleeding edge, this conversation offers a field guide to the infrastructure behind intelligent computing.

  continue reading

157 episodes

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