orientation dramatically changes everything and presents challenges as the power must be delivered in a smaller area and distributed at higher amperage .
Steven Carlini , Vice President of Innovation and Data Center , Schneider Electric
Finally , you have cooling concentration and piping coming in and out of the server , which leads to manifolds and cooling distribution units , which are all new factors .
You cannot spread out the load because these servers and individual GPUs run in parallel and are connected through fibre . The fabric , or the InfiniBand , is running at high speed , which means it is extremely expensive . By trying to spread the load apart , you would spend a lot of money deploying this fibre network for all these processors . In the servers , each GPU has a network connection and each one has a fibre connection . This presents a large cost in addition to the real estate costs .
Due to these high costs , we are seeing a high desire to deploy these servers , high-density racks and clusters with as small a footprint as possible . Due to their design , they operate very close to capacity and maximum thresholds . Previously , when you had 10kW per rack , you were usually running at 3kW per rack and it spiked up occasionally to 10 . The new training clusters run at capacity , so if you design it for 100kW per rack , it will run at 100kW per rack .
It ’ s important to be cognizant of running at capacity and use software tools to manage your environment as you are on the critical edge and have marginal buffer .
AI workloads are expected to grow significantly . What strategies and innovations can organisations implement to address the increasing power demand in existing and new data centres , optimising them for AI ?
There are two approaches to consider . Starting from scratch would be the preferred option , as it allows for the optimisation of the power train with fewer step downs of voltages and transformers . For existing environments with sufficient power capacity , technologies like rear door heat exchangers can be fitted to current racks , providing higher densities , such as 40 to 70kW per rack . Depending on available power , retrofitting the current site can be done . However , if power is limited , a very dense application may result in excess floor space that remains vacant .
Recommended strategies involve modelling everything with Digital Twins , from power systems to IT rooms . This allows organisations to better visualise
the implications before deploying in the physical world .
With AI applications placing strain on power and cooling infrastructure , how can data centres balance energy efficiency and environmental responsibility with the demands of AI-driven applications ?
Currently , a permit for the construction of a data centre requires a demonstration the facility can operate at a very high efficiency level or very low PUE . In many cases , to obtain a permit , a data centre must show that it will be powered with a certain amount of renewable sources or use PPAs or renewable energy credits . It ’ s a given that these centres must be designed to be highly efficient .
Liquid cooling significantly contributes to making the cooling more efficient . The types of neon economizers and heat rejection used outside for further liquid are also important considerations . Designing data centres to be as efficient as possible and using the highest percentage of renewable power is the approach that leading companies are taking .
Net zero goals and the need to address physical infrastructure redundancy are important . Any design that ’ s considered can be modelled to see the effects on efficiency . With a shift in the utility grid to more distributed resources and more sustainable sources like wind and solar , sustainable strategies can be adopted .
With this changing utility grid , software is available that allows you to pick from different sources based on your
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