3 min read

Google Cloud GPU: The Benefits of Using GPUs in the Cloud

As disruptive technologies like machine learning and artificial intelligence continue to change the modern business landscape, GPUs are in high demand. Not only do GPUs offer faster performance than traditional CPUs but they can also save time, free local resources, and even reduce costs. 

Let’s take a closer look at Google Cloud GPU and explore the multitude of benefits. 


What Is Cloud GPU?

A GPU is a Graphics Processing Unit, not to be confused with a Central Processing Unit (CPU).

GPUs are specialized programmable electronic circuits responsible for rendering images on a computer screen. Though typically associated with graphics-intensive workloads for video and 3D images, GPUs have proven to be an invaluable asset for programmers and engineers seeking to streamline workloads with machine learning.

Not to mention, GPUs are transforming the work of data scientists and offer significantly faster performance than CPUs when training neural networks associated with deep learning in AI.

A GPU has a parallel structure that enables higher efficiency compared to CPUs, meaning that GPUs can simultaneously perform multiple small processing tasks broken down from a larger task rather than performing a single large task in sequence. Many modern GPUs have hundreds or even thousands of processor cores which enable them to handle complex tasks significantly faster than a CPU.

And as impressive as physical GPUs are, they’re even more useful in the cloud.

Cloud GPUs are computer instances with robust hardware for running applications to accommodate substantial artificial intelligence (AI) and deep learning models in the cloud. Since these workloads are executed in the cloud, Cloud GPUs do not require a physical GPU on your device.


How Cloud Computation Works

Cloud computation eliminates the burden of physical hardware and technology infrastructure by using remote servers to store and access data. Broadly speaking, cloud computation can be divided into:

  • Private cloud - Proprietary cloud environment accessible to only a single organization featuring customized architecture, advanced security, and extensive resource management.
  • Public cloud - Data management and storage using the public internet that allows organizations to scale easily and pay for resources as needed.
  • Hybrid cloud - Combination of public and private cloud models allowing companies to store confidential internal data and access it through applications in the public cloud.

Working in the cloud gives companies the ability to simplify operations and reduce overhead with virtual workstations while promoting experimentation and innovation. This is especially true when it comes to GPUs.


The Benefits of using GPU in the Cloud

Google Cloud GPUs are an outstanding option for companies in need of high-performance computing. Even smaller organizations with limited resources can leverage the power of GPUs on Google Cloud to optimize their processing needs.

Here are some key benefits to consider.

Saves Time

Faster rendering times translate to dramatic improvements in workflow. Using Cloud GPUs, teams can accelerate machine learning modeling times from hours to mere minutes. This gives engineers more time to iterate solutions and focus on other projects.

Frees Local Resources

Cloud GPUs don’t consume local resources, which helps computers run more efficiently. A demanding machine learning model or complex rendering task can make a local computer practically unusable when running. But by outsourcing computations to the cloud, local resources are freed up so devices can operate without interruption when running a GPU instance.

Easy Scalability

Whenever workloads are ready to increase, you can easily add GPUs on demand with Cloud GPUs. And if you need to scale down, GPUs can also be removed quickly. 

Cost Reduction

Buying physical GPUs can be expensive, especially for large-scale operations. But with Cloud GPUs, costs are significantly reduced. Most major cloud providers like Google Cloud allow you to rent GPU servers on an hourly basis at an affordable rate. 


How To Get Started with Google Cloud GPUs

When you choose Google as your Cloud GPU provider, Google Compute Engine provides GPUs that you can add to your Google Cloud virtual machine (VM) instances, which can be implemented to accelerate particular workloads on virtual machines. Google also offers a range of different GPU models to choose from which are tightly integrated with Google Cloud Machine Learning.

If you need a custom, enterprise-level GPU solution for your organization, get started with Promevo.

At Promevo, we help you harness the robust capabilities of Google to accelerate the growth of your company and give you the momentum you need to achieve your most ambitious business goals. As your trusted service partner, Promevo supports your business with a robust suite of services, including:

  • Advanced Automation and Precision Control 
  • End-to-end Solutions Specific to Your Needs 
  • Advisory Workshops 
  • Certifications and Google Expertise 

With our expert consultation, comprehensive support, and exceptional service from end-to-end, you can drive productivity and accelerate the growth of your business.


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