Webcast: Considerations when Deploying GPUs as AI Infrastructure (IBM & NVIDIA)

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This webinar explores options for enterprises looking to deploy GPUs, on the journey to AI. Join IBM and NVIDIA as we explore the opportunities and options for organizations looking to transition from traditional servers to GPU powered systems, specifically for AI workloads. We’ll explore the impact GPUs can have on performance, by workload. We’ll look at recent innovations in using GPUs for general purpose computing, and we’ll look at ways in which organizations can optimize their GPU infrastructure deployments. In this webinar you’ll learn:

  • Candidates for GPU acceleration – Explore the types of workloads where GPUs make sense
  • Recent GPU innovations – Learn about the state of the art in GPU acceleration, from speeds & feeds to AI-specific technical advancements
  • Performance optimization – Explore the impact I/O and CPUs can have upon GPU performance for AI workloads

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Brad Davidson

Global Account Manager for IBM Account

Brad Davidson is the Global Account Director for the IBM account at NVIDIA. Brad is a recovering silicon engineer with 29 years of industry experience. He earned his Electrical Engineering degree from the University of Cincinnati and has held various positions in engineering, marketing and sales developing CPUs, custom ASICS and large scale supercomputers for Sun Microsystems, Texas Instruments, Transmeta, Appro and Dell.

Michael Boros

IBM Cognitive Systems
AI Infrastructure Marketing


Mike has 25 years of product management and technical marketing experience, working with information technologies for large enterprises. Mike’s technical background is broad, spanning: servers, storage, HPC, systems management, analytics, and AI. He’s worked both as an independent consultant and as an employee for some of the industry’s most influential technology leaders including: Oracle, Dell, HDS, and IBM.

Webcast: Considerations when Deploying GPUs as AI Infrastructure (IBM & NVIDIA)
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