Beyond Efficiency: Scaling AI Sustainably

• URL: http://arxiv.org/abs/2406.05303v2
• Date: Sat, 22 Jun 2024 00:33:22 GMT
• Title: Beyond Efficiency: Scaling AI Sustainably
• Authors: Carole-Jean Wu, Bilge Acun, Ramya Raghavendra, Kim Hazelwood,
• Abstract summary: Modern AI applications have driven ever-increasing demands in computing. This paper characterizes the carbon impact of AI, including both operational carbon emissions from training and inference as well as embodied carbon emissions from hardware manufacturing.
• Score: 4.711003829305544
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Barroso's seminal contributions in energy-proportional warehouse-scale computing launched an era where modern datacenters have become more energy efficient and cost effective than ever before. At the same time, modern AI applications have driven ever-increasing demands in computing, highlighting the importance of optimizing efficiency across the entire deep learning model development cycle. This paper characterizes the carbon impact of AI, including both operational carbon emissions from training and inference as well as embodied carbon emissions from datacenter construction and hardware manufacturing. We highlight key efficiency optimization opportunities for cutting-edge AI technologies, from deep learning recommendation models to multi-modal generative AI tasks. To scale AI sustainably, we must also go beyond efficiency and optimize across the life cycle of computing infrastructures, from hardware manufacturing to datacenter operations and end-of-life processing for the hardware.

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