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of existing or planned data centres and reduce the need for new ones , significantly lowering TCO .
3 . The latest silicon technology is very expensive
Maximising performance in current AI accelerator products is a key area of focus for the industry . However , the current approach to meet this AI demand is to add more silicon area , more power and , crucially , more cost .
Earlier this year , Nvidia reported its new chip , the Blackwell GPU , would cost US $ 30,000 to US $ 40,000 , with the costs of its development amounting to a massive US $ 10 billion . It ’ s a process of chasing diminishing returns . stable due to a focus on efficiency . AI represents a much larger challenge , but this focus on efficiency shows the industry what ’ s possible when it innovates .
As well as reducing costs , optical AI acceleration can play a key role in reducing the TCO and the environmental footprint of AI .
The current trajectory is unsustainable , both for the planet and AI development – the projected TCO for AI data centres is far too high to meet requirements in both areas . It ’ s worth reminding ourselves that a sustainable approach also aligns with a cost-efficient one . With the help of new technology like optical AI acceleration , data centres can reduce their TCO and create a cycle of sustainable investment . p
What ’ s needed is a cost-effective way of using existing optical and electronic technology in data centres . Optical processors can leverage such infrastructure , removing the need to use expensive new silicon technology . Therefore , if we combine these cost savings with reduced power consumption and less cooling , the TCO is a fraction of a GPU .
How reducing TCO can help the industry moving forward
If we look back to 2015 – 2019 , even as workloads trebled , data centre power demand remained relatively
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