Industry Report - Capacity Gap Analysis for AI Processing

AI is becoming the most significant paradigm shift in history, with a direct impact on the global economy. Generative AI, in particular, is expected to greatly enhance productivity within work processes. Some studies estimate that Generative AI could contribute between $2.6 trillion and $4.4 trillion annually—for comparison, the EU’s entire GDP in 2023 was $17.1 trillion. This Industry Report estimates the current and projected computing needs for generative AI training and inference in 2024 and 2030, while identifying key challenges and potential solutions to meet these demands. The main challenges in AI development include data availability, scalability limitations of centralized systems, power constraints, and challenges in accelerator manufacturing. By 2030, creating new distributed and decentralized systems for AI training—leveraging a continuum of HPC, cloud, and edge resources—will be a critical aspect for meeting the processing demands of AI training and the low-latency requirements of AI inference. This underscores the urgent need for strong collaboration between supercomputing, cloud computing, and edge computing, as well as the development of a management and orchestration platform to create cloud-edge environments that address the demands of AI processing workflows and the high-performance, low-latency requirements of their components.

 

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