Myth: Training AI models requires enormous energy consumption that's solely to blame for the carbon footprint of large-scale AI deployments.
Reality: While it's true that the training process of some large language models and deep neural networks can be energy-intensive, the real carbon footprint contributor lies in the inference phase, which accounts for approximately 95% of an AI model's total environmental impact.
Most people are unaware that the majority of an AI system's energy usage occurs when it's in production, processing millions of requests per second. For instance, a single recommendation algorithm used by a popular streaming service can generate as much carbon emissions as a small country. Therefore, optimizing the inference phase and focusing on energy-efficient hardware and deployment strategies are crucial to mitigating the environmental impact of large-scale AI deployments.
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