The Economics of High-Performance Computing on Accelerated Artificial Intelligence

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What accelerated computing means for AI

High performance AI/ML for industry-leading or world-changing applications is different. The most demanding computational work in things like gene therapy, drug research, deep image analysis, and deep learning require specialized hardware, such as GPUs. There are also separate edge applications requiring specialized compute near the data sources. We see this in smart and secure spaces including theme parks and stadiums, cruise ships and resorts, schools and airports, and factories and transportation, to name a few. In these spaces, real-time decisions are required across thousands of sensors per facility, and this requires edge hardware enabled by GPUs.

With our ecosystem of technology partners, we can help you identify the right hardware and infrastructure that aligns with business strategy and goals, and we then work with you to implement the right tools to prepare your data infrastructure for a future with real-time AI and HPC.

Setting up your data infrastructure for real-time compute

Once a vision is set for how the data architecture can be enhanced for HPC and edge AI, the next step is to identify which pieces can be bought, which are better used as a service, and which could be built by the enterprise. These are not just technology considerations but instead they impact the wider business strategy and spending. Indeed, shifting the data architecture is a business decision. The complexity of the challenge and the enormous diversity of differentiating AI applications takes specialized domain expertise across technology ecosystems, systems integration, change management, and business strategy. Deloitte brings vertical specialization with cross-solution application in AI and HPC architecture to help drive the transformational shift away from post-transaction reporting to data attenuating business process at the point of action.

Succeeding with AI requires computational power. Using GPU-accelerated computing for model creation and deployment in application delivers essential time savings, higher accuracy, and a greater capacity for experimentation. As enterprises refine and expand their AI strategies, the clear call is to identify where accelerated computing can be used to enhance existing capabilities and accelerate the entire AI lifecycle.

Discover how GPU enabled AI model development and implementation accelerates internal efficiency and reduces solution time to market. Going beyond CPU-driven machine learning and AI modelling unlocks tools and capabilities in both solutions and infrastructure that create more insightful models, scalable architecture and higher level of customer engagement and efficiencies!


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