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From cost center to competitive edge: The strategic value of custom AI Infrastructure
- Custom AI infrastructure has become an asset and considered a strategic practice for better business strategy. AI infrastructure is significant to enterprises, and it is essential to invest in a vendor that is technologically capable of serving a reimagined enterprise operating world. Training and inference for particular AI workloads call for a groundbreaking custom AI infrastructure. Enterprises must invest in infrastructure that meets the demands of workloads 24/7. Amazon SageMaker is a managed AI infrastructure for customized solutions with open-source frameworks like TensorFlow and PyTorch. Open-source frameworks are critical for AI development, but custom infrastructure is required to handle the scale and complexity of modern AI workloads. Nvidia has taken the lead in AI infrastructure due to its GPU dominance; however, other competitors, including Intel and AMD, are looking to close the gap. Custom AI infrastructure is not just a cost center and, therefore, recognized as a strategic investment that provides a significant competitive edge for businesses.
- To balance flexibility and cost-efficiency, some enterprises now realize that they need a hybrid approach of cloud and on-premises infrastructure. This is because the pay-as-you-go model of cloud computing can become very expensive for those running AI models 24/7. As agentic AI or Artificial General Intelligence (AGI) becomes a reality, it will bring fundamental changes in the game. The future of AI infrastructure will involve the emergence of industry-specific models like BloombergGPT for financial services, and this necessitates custom infrastructure. Indeed, custom AI infrastructure is more than a technological concern and is about close partnerships with vendors to optimize specific infrastructure for particular needs. The right infrastructure is vital in transforming AI from an experimental phase into a business driver.
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