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  "notes": "The chart compares Accelerator Years, Energy Consumption (MWh), and Net CO2e (metric tons) for models Meena, T5, GPT-3, Gshard-600B, and Switch Transformer.",
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      "text": "Major factors that drive the carbon emissions during model training are the choice of neural network (esp. dense or sparse), the geographic location of a datacenter, and the processors.",
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      "text": "Companies with heavy AI workloads including NVIDIA and AWS estimate that 90% of the energy consumption comes from inference and 10% from training.\nGoogle evaluated the energy and CO2 budget of five popular large language models and proposes simple formulas for researchers to measure and report on these costs when publishing their work.",
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