For many years, the US company Nvidia shaped the foundations of modern artificial intelligence. Its graphics processing units (GPUs) are a specialised type of computer chip originally designed to handle the processing demands of graphics and animation. But they’re also great for the repetitive calculations required by AI systems.
Thus, these chips have powered the rapid rise of large language models – the technology behind AI chatbots – and they have became the familiar engine behind almost every major AI breakthrough.
This hardware sat quietly in the background while most of the attention was focused on algorithms and data. Google’s decision to train Gemini on its own chips, called tensor processing units (TPUs) changes that picture. It invites the industry to look directly at the machines behind the models and to reconsider assumptions that long seemed fixed.
