Chelsea Robinson, Product Marketing Manager, Digital Realty, highlights exactly what makes a colocation provider AI-ready, and how today’s enterprises can identify progressive colo companies. What started as a dystopian, futuristic, robots-take-over-the-world idea is currently becoming a universally accepted approach for running businesses. Apparently all businesses are adopting some kind of AI simplify approaches for company challenges to stay ahead of competition, and improve customer experience.

But how can companies implement AI into their operations? It comes down to their own IT environment. Not each colocation facility is properly equipped to support AI workloads.

This presents the unique opportunity to research what truly qualifies a data center to encourage artificial intelligence: the ability to encourage high-density workloads and power requirements, in addition to the ability to present advanced cooling technology to maintain those workloads stable and running.

server colocation

High Density Workloads

server colocation is a theoretical proof that was designed in 1961 that shows a upper limit of the number of computations could be processed for every kilowatt-hour. At a basic level, computers need to operate within the laws of physics and energy use is caused by computing power and generates heat. Where does this leave organizations that desire to maximize or optimize their power?

Power prototyping GPUs (graphics processing units) can do complex mathematical algorithms much faster and economically than a regular CPU (central processing unit). One example of this is NVIDIA’s DGX-1 servers, in which the GPU technology process and could consume 140 times faster than servers. This would have a server that is CPU-only within 711 hours. Having this kind of deep learning it only takes 5 hours with the DGX-1. This presents businesses with an chance to better their data processing and company functionality in a fraction of the time. Think of what a business could do with 1-2 petaflops of calculating power and the way that might help a company accomplish their business goals quicker.

Power Up The quantity of data that machine learning applications require in order to procedure the electron loads (think sophisticated algorithms, predictive modelling and much more ) raises power needs dramatically.

When energy-demanding artificial intelligence software are known to use over 30 kW per rack, electricity demands regularly exceed standard data centre power standards. Information centers and colocation providers need to ensure they have redundant power plans to minimize downtime.

With higher density workloads comes more power, which translates to more heat. Not many data centers are built to support the requirements placed on these sorts of gains in power and cooling requirements since power consumption can translate to a need for alternative methods that were cooling beyond fan cooling system. Gartner predicts that more than 30 percent of data facilities that fail to prepare for AI will no longer be economical to operate by 2020. If cooling capabilities are not in place, your IT infrastructure will fail to function and will impact your organization.

In the core of running seamless artificial intelligence applications is your user experience (UX).

One popular method of cooling, especially for data centers with AI workloads, is cooling. Direct-to-chip liquid cooling is used by some solutions system, but some use water to cool the atmosphere. Whatever the procedure, liquid cooling has important benefits over fan cooling–in certain instances reducing power usage by 20% (from 1.5-2.0 PUE to below 1.1).

While liquid cooling is successful, it will increase water consumption. For information centers which use this methodology, water usage may be lower compared to its liquid cooled cousin. It is essential that colo providers tackle the impact by relying upon water for cooling rather than water that is potable, which makes them equally green and AI-ready. This enables the colo customer to guard both their investment and the environment.

In the core of conducting seamless artificial intelligence software is your user experience (UX).

In case your colo partner is not assuring at least five nines (99.999%) of uptime –that’s less than six minutes per year of downtimethen you may not have a highly reliable partner. Reliability is critical for any organization. In reality, Gartner calculated in 2014 that businesses could lose well over $300K on average in only an hour downtimea figure that has improved over the last five years.

Outstanding User Interface

Customers should anticipate out of their colocation provider excellent service and undisrupted data move in a secure environment, especially when talking to AI applications. Choosing the right data centre provider is a tactical decision that is so essential to achieving your business objectives.

What to Look for in an AI-ready Colocation Provider