GPT-4, PaLM, Claude, Bard, LaMDA, Chinchilla, Sparrow – the list of large-language models on the market continues to grow. But behind their remarkable capabilities, users are discovering substantial costs. While LLMs offer tremendous potential, understanding their economic implications is crucial for businesses and individuals considering their adoption.
While LLMs offer tremendous potential, understanding their economic implications is crucial for businesses and individuals considering their adoption.
First, building and training LLMs is expensive. It requires thousands of Graphics Processing Units, or GPUs, offering the parallel processing power needed to handle the massive datasets these models learn from. The cost of the GPUs, alone, can amount to millions of dollars. According to a technical overview of OpenAI’s GPT-3 language model, training required at least $5 million worth of GPUs.
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