3 Business Benefits of GPU Virtualization
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A decade ago, many businesses considered machine learning an exciting concept that still had a long way to go before it became a practical tool. Today, more and more companies consider using ML to provide their organizations with indispensable customer insights and business tools that make previously unfeasible jobs possible.
Despite machine learning’s many uses, there are still cost challenges. In addition to the initial cost of building ML infrastructure, costs come from the work data scientists spend configuring hardware, including GPUs, CPUs, and other tools. This work means highly paid data scientists who are key to machine learning research and analytics are spending much of their time working on hardware instead of ML problems.
When teams work with the right machine learning infrastructure partners, they allow these highly paid professionals to dedicate more of their day to extracting insights and therefore helping their organizations get the most out of their investment. Let’s look at a couple of ML infrastructure considerations that are key to scalability.
Machine learning projects are fast-paced and ever-changing. Data scientists need environments that are fast, flexible, and scalable. The hybrid cloud satisfies all three of these needs through the combination of on-premise and cloud resources.
The on-premise hardware ensures that teams have low-latency hardware on demand. When built with modular, white-box components, teams can quickly adapt the configuration to meet their projects’ computing needs. Additionally, because this is a hybrid cloud setup, when teams reach 100% capacity, they can quickly augment their hardware with cloud resources.
Hybrid cloud setups also help cut cloud costs by using on-premise resources until the team hits max capacity. However, because teams are building their ML hardware, they must ensure those parts are optimized for deep learning.
Machine learning hardware has significant impacts on latency, energy costs, and flexibility. The key to choosing ML hardware is to find hardware that allows you to perform complex calculations in parallel while reducing power usage.
The processors you choose will have one of the biggest impacts on your calculation-to-power ratio. Consider for a moment three types of processors: CPUs, GPUs, and ASICs. CPUs tend to be less expensive than other types of processors but are limited because they process in sequence. On the other hand, GPUs can process in parallel, allowing ML algorithms to perform many more calculations in the same amount of time. Another type of processor used in ML is an ASIC (application-specific integrated circuit). These processors have less flexibility than a GPU, but their focus allows them to increase performance while saving costs.
When building on-premise hardware for the hybrid cloud, businesses must also consider hardware like memory, system buses, and storage. Optimizing each component to operate at peak performance in tandem with each other will allow data teams to better leverage their available resources before turning to the cloud.
How much time do you want data scientists to spend on ML problems? Their productivity is directly related to how much time they can spend on machine learning versus other tasks like managing hardware. Intequus helps organizations simplify the machine learning infrastructure decision by providing full-lifecycle hardware and support. From designing to decommissioning, we can help you build hardware that ensures consistent performance. Talk to an expert today.