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Four (More) Ways To Prepare Your Data Center For AI

Mohammed NaserMohammed Naser

Equip your data center to better manage heavy workloads from AI/ML/DL. Optimize GPU performance and pick the right networking system to prepare.

Earlier, we have talked about the extensive and consuming demands of AI/ML/DL. We went on to discuss how your data center can prepare to accommodate such workloads in its environment. Here are some more ways in which your data center can take on AI and its subsets and their operations:

Optimize GPU Performance

AI/ML/DL applications are compute-intensive. Therefore, GPUs are the preferred resources for all processing related to these applications. However, training data sets mostly except RAM, making a large number of files hard to store and manage. Hence, strategizing a balance between GPU and CPU power, along with memory and network bandwidth for both GPU servers and infrastructure storage, is critical for an efficient data center.

Choose Networking System

Back-fitting new workloads like AI/ML/DL into the existing network infrastructure is poor practice. The current infrastructure is unlikely to support complex computations of that nature. For fast and efficient data delivery, AI/ML/DL require low latency, high bandwidth, smart offloads, and high message rate. Therefore, picking the right network transport becomes vital to efficiency.

Ensuring Data Protection

The storage system for AI/ML/DL is significantly faster than others in a data center. Consequently, recovering it from backup after a complete failure takes longer and disrupts ongoing operations. The read-mostly nature of DL training makes it a good fit for distributed erasure coding where the highest level of fault tolerance is already built into the primary storage system, with a minimal difference between raw and usable capacity.

Capacity Elasticity

Accommodate any size or type of drive, so that as flash media evolve and flash drive characteristics expand, data centers can maximize price/performance at scale, when it matters the most.

Performance Elasticity

AI data sets need to grow over time to improve the accuracy models further. So, storage infrastructure should achieve a close-to-linear scaling factor, where each incremental storage addition brings the equivalent incremental performance. This allows organizations to start small and grow non-disruptively as business dictates.

Reliable Data Center For Your Cloud Needs

When it comes down to storing and accessing large amounts of data securely through your business or organization, cloud services may be the solution that you're looking for. VEXXHOST has two data center regions within Quebec for high-density power exactly where you want it - in Canada. We can also give you high-speed direct access to Silicon Valley's Tier-1 carriers and blazing connectivity through our Santa Clara public cloud region.

If you are interested in knowing more about our data center specs or are interested in a public or hosted private cloud environment, get in touch!

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