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Will the cost of scaling infrastructure limit AI’s potential?
The race is now on to scale AI workloads while controlling infrastructure costs.
“Current AI systems are still being explored at a rapid pace and their progress can be limited by factors such as energy consumption, long processing times, and high compute power demands,” Jamie Garcia, director of Quantum Algorithms and Partnerships at IBM told VentureBeat. “This gives them the natural potential to accelerate AI applications that require generating complex correlations in data, such as uncovering patterns that could reduce the training time of LLMs,” Garcia said. Kirk Bresniker, Hewlett Packard Labs Chief Architect, HPE Fellow/VP has numerous concerns about the current trajectory of AI scaling.
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