大型語言模型：全新版本的摩爾定律？
Original: Large Language Models: A New Moore's Law?
In late 2021, the AI field witnessed an unprecedented explosive growth in large language models (LLMs). From OpenAI's GPT-3 at 175 billion…
本文探討大型語言模型（LLM）參數規模以驚人速度增長的現象，並將其與「摩爾定律」相提並論。然而，這種「越大越好」的趨勢伴隨著極高的算力成本、碳排放以及技術壟斷風險。Hugging Face 呼籲社群關注模型民主化，並透過開源合作（如 BigScience 專案）與高效能技術（如蒸餾、量化）來打破巨頭壟斷，尋求更永續的 AI 發展路徑。
In late 2021, the AI field witnessed an unprecedented explosive growth in large language models (LLMs). From OpenAI's GPT-3 at 175 billion parameters to the Megatron-Turing NLG jointly developed by Microsoft and NVIDIA reaching 530 billion parameters, the growth curve of model scale seemed to be replicating — and even surpassing — Moore's Law in the semiconductor world. Yet this "Scaling Hypothesis" — the idea that simply increasing parameters and compute will improve model intelligence — also raised significant concerns.
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