The graphics processing unit (GPU), which initially came to the scene to improve the visual graphics of a computer, has become the key component of some of the extremely powerful computers today.  

GPU as a Service (GPUaaS) Market size is set to surpass USD 15 billion by 2027 and is anticipated to grow at a CAGR of over 40% from 2021 to 2027, according to a new research report by Global Market Insights Inc. 

GPUs are now the most important aspect of computing technology, both for personal and business computing. Though CPU cores are significantly faster and more powerful on their own, the sheer number of GPU cores and their ability to process tasks in parallel vastly outnumber the CPU performance. A computer with a powerful GPU takes much less time than the one without GPU to apply special effects or filters to videos and improve the screen resolution. 

You could assume that GPUs are prohibitively expensive due to their numerous functionalities, however they free up your local resources for other purposes. On a GPU, operations that would take 8 to 9 hours on a CPU, can be accomplished in as little as 8 to 9 minutes. Furthermore, you can cut costs by forming a joint venture with a public cloud provider and pay only for what you utilize. For example, you may run five GPUs for five hours on a public cloud for as little as $0.70 per hour and hence outsource this requirement to the cloud.  

According to Statista, “the GPU market was valued at 25.41 billion U.S. dollars in 2020, with forecasts suggesting that by 2028 this is likely to rise to 246.51 billion U.S. dollars, growing at a CAGR of 32.82 percent from 2021 to 2028.” 

GPU market size worldwide in 2020 and 2028 (expected) ACE Cloud Hosting GPU market size worldwide in 2020 and 2028 (expected) 

  Understanding GPUs- How do they operate?  

GPUs are special electronic circuits that swiftly operate and update memory to fasten the image creation in a frame buffer and its screening on display devices. They operate as translators. All the pictures that we see on the desktop screens are made up of minute dot size areas called pixels. Most typical screens arrange more than 2 million pixels and further need to analyze each pixel to form an image. In this case, the CPU operating jointly with the software applications transfers the data to the graphics card. The graphics card then analyzes the binary data to build it into a picture and displays it on the screen. These cards even speed up the process for computers to handle huge workloads when creating 3D images. 

GPUs are divided into two categories. The first is an integrated GPU, which shares memory with the CPU, while the second is a discrete GPU, which has its own card and memory. NVIDIA, AMD, and Radeon are some of the well-known GPU manufacturers. 

Why use GPU in the Cloud? 

Cloud GPUs are computer instances that provide hardware acceleration for an application without deploying GPUs on the user’s local device, but rather using GPUs on a cloud service. They provide more flexibility and bandwidth than the CPU’s L1 cache, resulting in lower hardware costs and total cost of ownership. 

They also offer the following benefits to enterprises: 

  • Simplified Compute Jobs: Speed up compute jobs like machine learning and data intensive applications with the help of cloud GPUs. Machine learning applications operate on numerous training processes. Therefore, GPU eases out the processes and swiftly carries out multiple, concurrent computations. It distributes the AI model training processes, in turn increasing efficiency and power.
     
  • Neural Networks: With the help of GPU’s feature to obtain data from memory simultaneously, parallel computations run efficiently to train artificial intelligence and deep learning models. GPUs possess high memory bandwidth, thousands of cores and operate 1000x faster than CPUs.
     
  • Train ML models: GPU enables the development team to create and train high quality models without any limits on model size along with minimal cost. As neural networks requires lots of calculations and matrix multiplications, and a GPUs SIMD architecture focuses more on logical cores, it results in enhanced speed, performance gains and controlled hardware expenses.
     
  • Streamline Services: You can create, train and develop AI and machine learning models with vast open-source libraries and 24×7 control over data. Cloud GPUs can complete building hefty models in a few hours with high-precision, scalability, and flexibility. This helps businesses quickly forecast their operations, profits, and outcomes in the market.
     
  • Instant Deployment: Boost your infrastructure with quick deployment of use cases such as texture mapping, video encoding, CAD drawings, etc. without pressurizing your computing systems. Cloud GPUs save time by helping developers leverage their rapid iteration feature with quick rendering process.
     
  • High Precision Performance: GPU offers high-performance accelerators for parallel applications such as financial risk management, climate modeling, geoprocessing, medical imaging, and video color correction. Using multi-level algorithm optimization, GPU instances surpass CPU performance. 

 

Industries leveraging Cloud GPUs  

Cloud computing is here to stay and has been becoming a viable option for many companies, leading to the emergence of cloud GPU services. Take a look at some of the industries that are leveraging cloud GPUs:  

  • Gaming Sector: GPUs elevate the performance of graphics used in games. This enhances the user experience of gamers as they can play games at a higher resolution and quick frame rates. With GPUs, videos and graphics are rendered easily and quickly in high-definition formats.
     
  • IT: Cloud GPUs are certainly important to the companies that deal with heavy computing, 3D visualization and machine learning. They accelerate the iteration of machine learning models, deliver accurate results for image recognition workloads and accelerate the performance up to 3x.
     
  • Banking: Defy market fluctuations and employ massive datasets at a faster rate. Elevate the financial services experience of your customers with risk management, quick data-driven predictions and response to critical requests.
     
  • Automotive: GPU conjointly with AI is transforming the power of automobiles. The industry leaders are leveraging GPU technology to enhance gesture control functionalities, natural language processing, deep learning and transforming vehicles into self- learning automobiles.
     

 

CPU Vs GPU: Which one is better? 

In order to handle workloads, both the CPU and GPU are essential. Few demanding games require both – a smarter CPU as well as a powerful GPU. GPUs and CPUs share a lot of similarities as well. To boost data throughput, a CPU and a GPU work together. 

The major distinction between their architectures is that a CPU is designed to handle a wide range of tasks fast, but the number of operations that can be run concurrently is limited. Due to tremendous parallelism, GPUs can process data several orders of magnitude quicker than CPUs, although GPUs are not as versatile as CPUs. A GPU cannot manage a computer’s input and output because CPUs have extensive and broad instruction sets. 

Let’s look at the detailed comparison between them: 

Basis for Comparison  CPU   GPU 
Abbreviation  Known as Central Processing Unit 

 

Known as Graphics Processing Unit 
Memory consumption  Consumes higher memory 

 

Requires less memory 
Speed  Takes hours to handle a single workload 

 

Completes a single task within a few minutes 
Cores  Contains less but powerful    cores   Contains thousands of cores  
Focus  Emphasizes low latency  Emphasizes high throughput 
Proficiency  Apt for serial instruction processing  Apt for parallel instruction processing 

 

Reasons why decision makers should care about enterprise grade GPUs 

Enterprise-grade GPUs can have a massive impact on the success of your business. They promote the highest performing infrastructure with the least amount of complexity. 

Some of the benefits of opting for enterprise grade GPUs are: 

  • Accelerate Processing Speed: Process massive data within minutes by utilizing GPUs in the case of a single server.
     
  • Leverage AI and Big Data: Get to the core of artificial intelligence and big data to forecast business outcomes and predict market insights related to your business operations with GPUs. Train your heavy AI models as much as you need and get more precise results in no time.
     
  • Overcome Low Productivity: Allow your businesses to handle a large clientele and popular projects. Free up your IT team of stressful operations and let the GPUs iteration feature work for you.  

 

Is Cloud GPU the future? 

Cloud computing has been the buzzword in the tech world for the last decade or more. The rise of cloud computing has impacted GPU usage across the industry and given rise to a new trend – on demand cloud-based GPUs. Cloud GPU is a new innovative technology, and it allows us to experience a high-performance graphics card in the cloud rather than in local machines. The main benefit of cloud GPU is that it does not directly compete with local graphics cards and is an efficient, cost-effective option for the end user.  

Cloud GPU is not only for professional gamers, but also for high-end graphic designers, video editors and others. Increased cloud platform providers are coming up with their offerings of on-demand GPUs as it’s so much more than just a trend in the next generation of cloud-based computing. Cloud GPU is the future of computing and is on the verge of hitting the mainstream. 

As GPU is required for every desktop and laptop computer, there would be no way to output an image to your display without it. Workloads benefit tremendously from GPUs because they provide massive gains in throughput. Future GPU performance improvements open a world of possibilities for demanding applications like computational graphics, computer vision, and a wide variety of high-performance computing.  

Do You Need a Dedicated GPU? 

Dedicated GPUs are designed to run graphic intensive applications and that is why they need to be more robust, powerful and should possess features for graphic applications. Although they are often more powerful than integrated graphics cards, this does not always indicate that they are the better option for you. The following are the two most important variables to consider when deciding whether to purchase a dedicated GPU or not: 

  • What is your total budget for the system you are looking to buy? 
  • The kinds of tasks and programs you are looking to run on your system.  

 

Why is Ace the best provider of Cloud GPUs? 

Ace offers powerful, energy-efficient, and dedicated high-end NVIDIA Tesla A2 GPU, providing an advanced computing platform for data centers, HPC (High Performance Computing), and AI at a nominal pricing. 

Our IaaS (Infrastructure as a Service) is an infinitely scalable platform that leverages innovative technology such as OpenStack and Ceph to assist businesses in achieving high-performing data centers. Start using the cloud with standard DDoS protection in seconds by deploying IOPS instances to the desired data centers in a single click. With demand-driven provisioning of spinning servers up and down, you can get quick access to NVMe drives, AMD Premium 64-bit chips, and AMD EPYC. Rely on our global network of data centers that are built, operated, and continuously monitored to meet your specific business requirements. So, what are you waiting for? Create an account and try Ace Cloud Hosting services today. 

To know and understand more about our services, call us at +1-855-223-488 (United States) or +91-981-110-4802 (India). You can also contact us via email (sales@acecloudhosting.com) or by visiting our website.

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