According to Pluralsight, 70% of organizational leaders surveyed reported that, in 2023, more than 50% of their infrastructure is hosted in the cloud, and 27% of those leaders reported that their cloud strategies have enabled them to better drive customer value.
Those are some impressive statistics that have helped to make businesses far more competitive. Thanks to the evolution of the cloud, this has been possible.
However, with every evolution of technology, there are pitfalls. Remember when the cloud became the rising star of the tech sector? It didn’t take long for the world to realize how the massive adoption of cloud technology would negatively impact the carbon footprint of so many major companies. With the cloud came the need to cool giant data centers, and every enterprise business had to think outside the box.
Then came containers and Kubernetes. Both of those technologies came out of nowhere to change the workflow of enterprise companies around the world. Very quickly those businesses had to come to grips that the likes of Kubernetes would pose serious challenges to their developers, admins, and operations teams. Why? Kubernetes is not an easy technology to master.
So, when generative AI came along, it was only a matter of time before the challenges made themselves known. Beyond the typical challenges of integrating AI into a business, there was another specter looming in the background, the increased demand for GPUs.
What Are GPUs and Why Do They Matter?
GPU stands for Graphics Processing Unit. Most people associate GPUs with drawing graphics on a computer display. But GPUs can do so much more, especially for large businesses that require massive amounts of computing power.
Traditionally, computing tasks are handled by the Central Processing Unit (CPU) of a desktop or server. For most applications, that works just fine. But with the rise of cloud and AI and Machine Learning services, the demand placed on CPUs very quickly outpaced the ability of traditional hardware.
Because of that, developers had to figure out a way to offload some of the computing processes; otherwise, servers would fail to meet the growing demand caused by these new technologies. This is especially so with AI, which requires massive amounts of power.
AI’s High Use of Resources
To understand why this is happening, we have to first examine how AI works. AI uses very complicated learning algorithms to locate structures and regularities within massive troves of data. As it discovers those data regularities, it acquires skills. Big data also works with large collections of data (which requires considerable computing power) but without the added stress of learning.
The learning portion of AI places an extraordinary demand on hardware, not only because it has to read through massive troves of data but also because the neural networks (mathematical computations that seek to mimic the neurons of the human brain) on which it depends must be trained.
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According to Moore’s Law, the amount of power used by technology doubles every two years. According to OpenAI, however, because of cloud and AI technologies, resource usage is doubling at a rate 7 times faster than previously tracked.
The only way to handle such resource demand is to offload more and more computations to GPUs. The problem is that GPU pricing skyrocketed during the pandemic. The good news is that GPU prices have dropped since their pandemic peaks. Even so, the cost is still considerably above MSRP. When you take into consideration the number of GPUs that are often required to handle such big computing loads, the cost can get prohibitive (especially when you’re working with multiple neural networks).
So, what’s an enterprise business to do?
Cloud GPUs Take the Spotlight
Instead of using in-house data centers (or simply a cluster of off-the-rack computers), many businesses are turning to cloud GPUs to handle AI computing. For many businesses, this is an ideal solution.
Why?
First off, it’s much cheaper than purchasing numerous (costly) GPUs that are powerful enough to handle the requirements of neural networks. And because most cloud GPUs are “pay as you use,” you can keep costs down with the ebb and flow of demand. When demand is down, you’ll use fewer GPUs, thereby saving you money. When demand is up, you’ll use more GPUs, thereby costing you more.
Another reason why offloading to the cloud is beneficial is because it will save your company on energy costs. The computational demand AI places on hardware requires considerable cooling efforts, which can cause your utility bills to skyrocket.
More than anything, it’s important to understand that cloud GPUs include very robust hardware acceleration, making them far more capable of handling the massive demand placed by AI workloads. The levels of power attained by cloud GPUs aren’t really viable with a consumer-grade GPU installed on a traditional desktop or server. So, when you need serious power, the best option is a cloud GPU.
These Are the Benefits of a Cloud GPU
Scalability
One of the reasons why businesses rely so heavily on the cloud is because it’s highly scalable. Along with that, cloud GPUs are far more scalable than traditional GPUs. You will also be able to easily add more GPUs from a simple point-and-click dashboard, so there’s also no need to crack open a server and add more physical hardware.
At the same time, you can easily scale down the number of GPUs when applicable. Both scaling options are typically done very easily and quickly within a cloud environment.
Cost Savings
As we’ve already mentioned, you’ll save considerable costs when employing cloud GPUs. You can even rent GPUs on an hourly basis, which also helps defray the costs over time.
Free Up Resources
When you employ cloud GPUs, you don’t have to worry about stressing your in-house systems with such a heavy computation load. This will free those machines up for other tasks, so you can do more with less, while ensuring your on-prem systems won’t cave under the added pressure of AI.
Time Savings
With cloud GPUs, you’ll enjoy faster neural net learning, faster rendering times, faster builds, and faster deployments. With all of that time savings, your developers and operational teams will be freed up to take on other tasks. Those engineers will also be free from having to monitor the hardware being used for AI training. No more concern for overheating servers, bottlenecks, or failed systems.
Sure, there will be other concerns, but when using cloud computing, those systems can be easily monitored and automated. Pulling off such a feat in-house can not only take considerable time and money but will also keep your staff so busy they might not have the availability to take on tasks like securing your network, developing new applications, iterating old applications, or patching vulnerabilities.
All of this is possible when your business opts to offload the considerable demands AI places on systems. If that sounds like something your business desperately needs, it’s time you turn to cloud GPUs to take on the heavy lifting of generative AI.
