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Evolution of Google TPUs and Large Language Models
Over a decade ago, Google recognized the increasing costs associated with artificial intelligence and the limitations of traditional GPUs, prompting the development of specialized TPUs optimized for neural networks, particularly matrix multiplication. Initial TPUs focused solely on inference, but advancements led to chips capable of both training and inference. The rise of large language models (LLMs), particularly post-GPT-3, sparked significant interest in creating advanced models, with the understanding that operational costs for deployment could become prohibitively high. As models scaled up, training costs escalated dramatically, from hundreds of thousands to potentially hundreds of millions of dollars. This situation underscored the importance of enhancing hardware, alongside algorithmic improvements, to optimize TPU performance for LLMs. The realization that existing chips were not solely focused on LLM workloads led to a decision to create hardware specifically designed for this burgeoning market, aiming to effectively utilize silicon real estate and cater to a potentially vast industry worth billions.