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In this episode of the series on strategic and functional approaches to scale Generative AI, I walk you through computational scaling, what it really means, why it’s a pressing topic today, and the different ways we can approach it. You’ll learn: What computational scaling means in the context of Generative AI Why computational scaling is crucial as AI models and agent-based systems grow more complex Key challenges including resource demands, data bottlenecks, model limitations, and coordination issues Three major scaling strategies: scaling up, scaling down, and scaling out Practical tips for each approach and how to choose the right strategy for your system A comparison of scaling up, down, and out to help you make informed decisions This series is designed for business leaders, technical teams, and curious minds who want to understand how to scale GenAI systems in a sustainable and efficient way. 👉 Future episodes will explore other dimensions like hardware selection, storage policies, and architectural scaling in more detail. https://youtu.be/ZM2F7WyhQbY?si=zfkHbkdL2ydp4doX
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