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Energy-Efficient Cloud Computing for Artificial Intelligence Training: Challenges, Techniques, and Future Directions

In: Recent Advances in Information Systems

Author

Listed:
  • Fayez Salma

    (Carl von Ossietzky University of Oldenburg, Department for Informatik)

  • Jorge Marx Gómez

    (Carl von Ossietzky University of Oldenburg, Department for Informatik)

Abstract

The rapid growth of artificial intelligence (AI) has amplified the computational and energy needs for training models, especially in cloud environments. This study takes into account the primary challenges in realizing energy efficiency for AI training, which are the growth of model sizes, inefficient use of resources, and infrastructural inefficiencies. A comprehensive review of novel solutions is provided, ranging from specialized hardware, software enhancements, to the operation of sustainable cloud infrastructure. Moreover, we highlight emerging trends such as AI-driven resource optimization, federated learning, and carbon-aware scheduling. Through our discussion, we highlight that there is an urgent requirement for energy efficiency to be a top priority alongside performance in the development of AI systems. We conclude by highlighting the requirement for cross-disciplinary collaboration to achieve a sustainable artificial intelligence future.

Suggested Citation

  • Fayez Salma & Jorge Marx Gómez, 2026. "Energy-Efficient Cloud Computing for Artificial Intelligence Training: Challenges, Techniques, and Future Directions," Progress in IS, in: Jorge Marx Gómez & Roopesh Kevin Sungkur & Sameerchand Pudaruth & Jan-Hendrik Witte & Gerrit Schuman (ed.), Recent Advances in Information Systems, pages 743-753, Springer.
    • Handle: RePEc:spr:prochp:978-3-032-14099-9_49
    DOI: 10.1007/978-3-032-14099-9_49
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