Why GPUs for Deep Learning? A Complete Explanation

The advent of AI has led to a paradigm shift in the industry. Deep learning, a technique that helps machines learn from large data sets, has provided a powerful reality-altering tool for businesses.

It is transforming machine learning (ML), allowing computers to recognize speech, grasp relationships between items, identify objects and understand their relationships, translate languages from text, and more.

According to a report, the value of the worldwide AI market as of 2022 is $136.6 billion. This is primarily due to the growing number of real-world applications for AI technology involving deep learning as its driving mechanism.

By 2030, it is anticipated that the global AI market would reach $1.81 trillion with growth in the profound learning sector.

AI market size worldwide in 2022 and 2030 (expected)

Deep Learning is primarily focused on programming systems to deliver specific results. But depending on the volume of data to be processed, training’s latency proves to be a challenge, especially with conventional CPUs.

Everything became faster and gave amazing outcomes after GPUs were introduced. Continue reading to learn everything you need to know about GPUs and why do you need one for your next deep learning project.

Understanding GPUs for Deep Learning

There are numerous applications of deep learning, both in research and commercial settings. Deep learning is based on artificial neural networks (ANNs) and is a popular technique for extracting precise predictions from large datasets.

To extract these predictions efficiently from all that information during the training phase of the model – the most resource intensive task, we need a lot of power to do so in the least amount of time. To put it simply, deep learning needs a lot of computing power to process all that information.

Any data scientist or machine learning enthusiast attempting to extract performance from training models on a large scale will ultimately approach a limit and start to experience variable levels of processing latency while performing various training operations on the dataset in order to achieve the desired outcome.

When training sets grow bigger, tasks that used to take only a few minutes to complete might now take many hours or even weeks.

The idea of a single powerful CPU core being used for high computational tasks is being replaced by numerous units with parallel processing that can perform better while handling enormous amounts of computational work efficiently as machine learning is progressing. These powerful units are emerging as GPUs.

Due to their capacity for parallel processing, which could generate graphics frames far quicker than CPUs, GPUs began to gain popularity, providing a smooth experience.

GPUs are also employed in various sectors where parallel computation is necessary, particularly for jobs that are transparently parallel and call for no manipulation.

If you are familiar with neural network mathematics, you should be aware that the matrices operation falls under the umbrella of parallel computation.

Also Read: Cloud GPUs: The Cornerstone of Modern AI

Do GPUs Outperform CPUs in Any way?

The CPU and GPU are both well-known silicon-based microprocessors that were developed from various perspectives. However, they are distinct from one another as we have talked about in our previous blog- The New Wave of Cloud GPUs: Revolutionizing the Business Landscape.

GPU is a processor designed to accelerate the process of rendering graphics. GPUs can process multiple pieces of data simultaneously, which is why they are widely used in deep learning, editing videos, and gaming applications.

Simply put, GPU gives your system the extra boost it needs to perform a specific task more efficiently.

The idea that deep learning needs a lot of powerful hardware is a widespread one. This hypothesis stems from the premise that a large number of computations are required for training models, and that sequential cycle CPUs are less efficient at handling these computations.

GPU has a large number of cores which makes it an ideal candidate to perform a great number of computations parallelly.

Utilizing a CPU for a deep learning task is common. But to process the new data, you’ll need a strong GPU because your dataset tends to grow with time. Since they are designed to handle various tasks and large volumes of data, GPUs are preferred over CPUs.

For instance, we consider a CPU as a car and a GPU as a bus. The car can fetch a maximum of 3-4 commuters in one turn while the bus can fetch 18-20 commuters in one turn.

Therefore, having a GPU enables your system to handle large datasets.

Let’s see their comprehensive comparison:

Cloud GPUs are in high demand as a result of the introduction of cutting-edge technologies like deep learning, AI, and ML. The modern-day cloud services provide you with an on-demand GPU which is easy to access.

You simply need to look for a cloud hosting company that offers cloud GPU, and they will provide you with the product you want. The cloud GPU will work efficiently even on large datasets to provide you with the best efficiency.

Also Read: How to Find Best GPU for Deep Learning

Top 5 Benefits of Using Cloud GPU

As more computing power is required to train the next generation of deep learning algorithms, many companies are turning to GPUs as their future tech.

Let’s look at some of the perks of using a cloud GPU:

High Scalability

The workload of your organization will eventually increase as it grows. A GPU with scaling capabilities is required due to the increased demand.

Cloud GPUs can help you achieve this by allowing you to easily and quickly add extra GPUs to handle your growing workloads.

Reduces Cost

You can choose cloud GPUs on rent that is offered at a reduced cost on an hourly basis rather than purchasing physically expensive high-power GPUs.

In contrast to actual GPUs, which would have cost you a lot even if you didn’t use them, you will only be paid for the hours you use the cloud GPUs.

Frees up Local Resources

Unlike real GPUs, which tend to take up a lot of space on your computer, cloud GPUs don’t use any of your local resources. This way, you can use your computer without strain by outsourcing the computational capacity to the cloud.

Instead of putting too much pressure on the computer to handle the burden and computational chores, just let it take control of everything.

Reduces Computation Time

Cloud GPUs allow users the freedom to iterate quickly while speeding up rendering. By finishing a process that used to take hours or days in a matter of minutes, you can save a lot of time.

As a result, your team’s productivity will considerably increase, allowing you to devote more time to innovation rather than rendering or computations.

Superlative Performance Precision

A core, machine, or system’s capacity for doing floating-point operations in a second is measured in floating-point operations per second (FLOPS), a measure of computing performance.

Through layered algorithm improvements, GPU instances improve the cost-to-performance ratio for single and double precision FLOPS.

High-Performance GPUs with Ace

Ace is a renowned public cloud service provider to small businesses, SMBs, accountants, CPAs, and IT enterprises. We offer customizable cloud solutions based on open-source and commercial technologies such as OpenStack, CEPH, KVM, and more.

We also provide the latest NVIDIA A series GPUs with resizable GPU instances, which are specially customized for AI & ML workloads.

Ace Public Cloud is hosted in tier 4 and tier 5 data centers to ensure high availability, data security, and redundant storage. We offer simple subscription plans and different compute instances with multiple price options no matter how big or small your requirements are.

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