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CIOs Navigate AI Portfolio Complexity
5 Nov
Summary
- CIOs must align AI workloads with optimal models, capacity, and infrastructure.
- Dedicated capacity offers predictable costs and better infrastructure visibility.
- AMD Instinct MI300X provides high-memory accelerators for demanding AI models.
Enterprise AI has evolved into a portfolio management challenge for Chief Information Officers. As AI adoption grows, matching diverse workloads to appropriate models, capacity, and infrastructure becomes paramount. A singular approach is insufficient, necessitating tailored deployment strategies.
Flexible, pay-per-token inference suits variable needs, while dedicated capacity is ideal for sustained demands with predictable cost and performance requirements. Managed Compute in Microsoft Foundry offers a solution by allowing control over workload decisions while managing the underlying serving environment.
Dedicated capacity shifts the economic model, provisioning resources based on anticipated needs rather than solely usage-based consumption. This provides better visibility into infrastructure usage and cost, enabling more accurate Total Cost of Ownership (TCO) calculations.
Running open models in production involves significant operational work. Managed serving environments reduce this burden, allowing teams to focus on development. The right model requires the right infrastructure, with accelerators like AMD Instinct MI300X offering high memory capacity crucial for large models.
AT&T's OTel2.0 initiative exemplifies this by utilizing managed compute and multiple GPU architectures, including AMD Instinct MI300X, for massive data processing. This portfolio-level decision-making allows for optimized performance and cost-effectiveness across diverse AI workloads.
