CoreWeave’s Brannin McBee on the future of AI infrastructure, GPU economics, & data centers | E1925

Apr 4, 2024

Brannin McBee, Co-founder and CDO of CoreWeave, shares insights from transitioning his company from cryptocurrency to AI infrastructure. He discusses the soaring energy demands of GPUs, emphasizing innovative cooling solutions and the significant costs tied to GPU operations. The conversation highlights CoreWeave's expansion with new data centers to tackle surging AI demands, and the competition in AI hardware, particularly against NVIDIA's dominance. McBee also explores the evolving landscape of AI applications and their implications for industries like advertising and e-commerce.

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ANECDOTE

CoreWeave's Crypto Origins

CoreWeave initially served the cryptocurrency market, focusing on Ethereum mining with GPUs.
This was a strategic entry point due to the clear arbitrage opportunity and the versatile nature of GPUs.

INSIGHT

Specialized Cloud for AI

CoreWeave's cloud infrastructure is purpose-built for AI workloads, unlike hyperscalers' general-purpose clouds.
This specialized approach offers a performance advantage, enabling CoreWeave to handle parallelizable workloads efficiently.

INSIGHT

Hyperscalers' Dilemma

Hyperscalers face an innovator's dilemma: rebuilding their infrastructure for AI or sticking with their existing model.
Rebuilding is necessary for optimal AI performance, but changing existing infrastructure is a significant challenge.

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Todays show:

Brannin McBee joins Jason to discuss CoreWeave’s transition from cryptocurrency to AI (3:36), the energy dynamics of GPUs (14:04), innovations in data center cooling (19:28), and the future challenges and impacts of AI infrastructure on industries like search, advertising, and e-commerce (45:36).

Timestamps:

(00:00) CoreWeave’s Brannin McBee joins Jason

(3:36) The founding story of CoreWeave and the transition from the cryptocurrency market to AI

(12:49) OpenPhone - Get 20% off your first six months at http://www.openphone.com/twist

(14:04) Energy reliance of GPUs, the future of data centers, and the cost and efficiency of GPU cloud infrastructure

(19:28) Direct-to-chip liquid cooling and other cooling solutions for GPUs

(24:52) Demand trends for GPU capacity and the growth of inference linked to user growth

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(30:28) LPUs Vs. GPUs

(34:13) Dominance of Nvidia in training models, implications of open source chips and chip architecture

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(40:00) Challenges in training large language models and the use of infrastructure as a weapon by big tech

(45:36) The potential impact of AI on search engines, advertising sector, and ecommerce

(52:46) Timeline for supply-demand balance in AI infrastructure and the operational feat of running multiple data centers

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