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Scaling smarter: How enterprise IT teams can right-size their compute for AI
How wisely IT and business leaders plan and choose infrastructure can keep them from being doomed to pilot purgatory or AI damnation.
Ironically, said Ozmen, also co-founder of AI platform company Riernio, “it’s simply because AI-centric design choices have overtaken more classical organization principles.” Unfortunately, the long-term cost inefficiencies of such deployments can get masked by deep discounts from hardware vendors, she said. According to Ozmen, successful scalers employ “a right-size for right-executing approach.” That means “optimizing workload placement (inference vs. training), managing context locality, and leveraging policy-driven orchestration to reduce redundancy, improve observability and drive sustained growth.” In the coming months, scaling options will expand further, as industry investments continue to pour into hyper-scale data centers, edge chips and hardware (AMD, Qualcomm, Huawei), cloud-based AI full-stack infrastructure like Canonical and Guru, context-aware memory, secure on-prem plug-and-play devices like Lemony, and much more.
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