D A T A C E N T R E P R E D I C T I O N S
Crucially , using optics for computation leads to a far more scalable approach so that the increasing demands of AI computation can continue to be met .
2 . Data centres are power hungry – and current hardware is too inefficient
The simple truth is that current hardware will not be able to efficiently match the surging performance demand required by AI models – it is either way too expensive or not efficiently possible with current chip technology .
Phil Burr , Director , Lumai and the insatiable demand , it is clear that now is the time to look at these different approaches . One such approach is to use technologies like 3D optics .
One of the limitations of data centres is the current use of power-hungry silicon chip based – AI accelerators . These current chips are unable to efficiently scale and provide the level of capacity needed for AI ’ s growing compute demand within reasonable power constraints . But if data centres can use an optical AI accelerator , the benefits of lowpower and energy efficiency , which are already seen in optical communications , can be utilised for computation .
AI is placing immense pressure on the energy consumption of servers in data centre racks . As the McKinsey study showed , “ average power densities have more than doubled in just two years , to 17 kilowatts ( kW ) per rack . . . and are expected to rise to as high as 30 kW by 2027 as AI workloads increase ”. With models like ChatGPT , energy consumption can be over “ 80kW per rack ”, while Nvidia ’ s latest chip may need rack densities “ up to 120kW ”.
The direct costs of supplying this energy and the infrastructure costs of cooling all of this power are significant ; each Watt of power consumed necessitates more cooling , more energy , more infrastructure and therefore more generated emissions .
Optical AI acceleration uses photons to compute instead of electrons and performs highly parallel computing . This means that optical AI accelerators use only 10 % of the power of a GPU ( currently used in data centres ) while also providing the necessary leap in performance . If optical computation can enable more efficient AI accelerators , this can increase the lifespan 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 $ 30,000 to $ 40,000 , with the costs of its development amounting to a massive $ 10bn . It ’ s a process of chasing diminishing returns .
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 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 . �
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