Improving the speed and energy-efficiency of AI agents
Researchers from MIT and Microsoft have developed a system called Murakkab that streamlines the design and deployment of complex AI workflows, improving their efficiency and reducing energy use. The system automatically selects optimal models, tools, and hardware configurations based on user priorities, such as cost or speed. Testing showed it significantly lowers computational and energy demands without sacrificing performance, addressing growing concerns about resource waste in cloud-based AI operations.
Researchers from MIT and Microsoft developed a system called Murakkab that makes AI workflows faster and more energy-efficient by automatically optimizing their design and resource use.
This improvement helps reduce energy waste and costs in cloud computing, which is becoming increasingly important as AI systems grow more complex and widely used.
This story highlights a practical solution to a growing challenge in AI, showing how innovation can lead to more sustainable and efficient technology use.
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