Consider the energy crunch: Global data-center power demand will more than double by 2030, per the International Energy Agency, forcing upgrades to grids, water systems, and connectivity. China’s state grids are embarking on a 5 trillion yuan ($722 billion) expansion explicitly for AI and data centers that is equivalent to 4% of GDP, according to Moody’s. The Qatar Investment Authority has announced a project worth $20 billion (9% of the nation’s GDP), to develop AI data centers and computing infrastructure. And in Korea, despite AI-related spending only accounting for 0.4% of GDP, the country’s recently established sovereign wealth fund is almost exclusively targeted at high-tech industries including AI and chips, while planning to deploy a war chest worth 5.7% of GDP over the next five years.
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The model does the work, not the code. The inference code should be generic autoregressive decoding that would work with any transformer checkpoint. If your generation loop contains addition-specific logic — manually pairing digits, threading carry state, indexing into specific positions — then the Python code is solving the problem, not the model.
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