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DESCRIPTION:As organizations scale their AI initiatives, many encounter persistent challenges with cloud GPUs: unpredictable costs, limited availability where capacity is needed, and underutilized resources. At the same time, modern AI applications,from real-time analytics to generative AI,require inference closer to users, something traditional centralized cloud regions were not designed to support.\nIn this webinar, Akamai experts will share how organizations can rethink AI infrastructure to improve GPU utilization, better control costs, and enable distributed inference, helping teams move AI workloads from experimentation to efficient production.\nWe'll explore:\n-The total cost of ownership: compare centralized GPU clouds vs. distributed edge inference\n-Industry-specific scenarios where latency, data sovereignty, and compliance drive architectural decisions\n-Quantitative models showing cost savings of 40-70% while improving response times\n-How to align AI infrastructure choices with business objectives and sustainability goals\nThis session equips C-suite and technology leaders with the strategic insights needed to make informed decisions about AI infrastructure investments and position their organizations for competitive advantage\n
DTSTART:20260430T060000
DTEND:20260430T070000
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LOCATION:Online
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SUMMARY;LANGUAGE=es-es:Tecnowebinars.com - :: Ep.2-Build series: From Cloud Regions to the Edge: Enabling Distributed AI Inference with Akamai GPUs
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