For IT leaders looking to harness its potential for their own organisations, the pace of development can feel bewildering. Enterprises are racing to make best use of their own data to either build their AI models or repurpose public models already available. But this can pose a significant challenge for the dev and data science teams involved.
Many enterprises will be acutely aware of issues around the general importance of data and IP and specific issues around data sovereignty and regulation, he adds. These range from CPUs to GPUs, even application-specific tensor processing units designed for neural networks, all with subtly different requirements, and all potentially playing a role in a customer's AI pipeline."Being able to support the full deployment of that infrastructure is absolutely top of mind," points out Sharp.
Hanging over all of this are the challenges associated with housing and powering this infrastructure. Just the density of technology required raises floor loading issues, Sharp explains."The simple weight of these capabilities is massive." And, as Digital Realty has found working with hyperscale cloud providers, floor loading requirements can increase incredibly quickly as projects scale up and AI technology advances.
The company has six highly connected campuses in the greater London area, offering almost a million square feet of colo space. But that doesn't exist in isolation, with over 320 different cloud and network service providers across the city."What we're seeing today is that customers need that full product spectrum to be successful," Sharp says.
At a hardware level, it has developed technologies such as its HD Colo product, which supports 70KW per rack, representing three times the requirement of certification for the Nvidia H100 systems which currently underpin cutting edge HPC and AI architectures.