To 10 Cloud Graphics Processing Unit Platforms for AI and Massive Workload

In the age of digital transformation, businesses are looking to artificial intelligence and big data analytics to drive innovation and empower their users with enhanced experiences. A graphics processing unit (GPU) can be used for various compute-intensive tasks, such as training artificial neural networks for machine learning or rendering graphics for video games or virtual reality. With the availability of GPU-enabled public cloud services, you no longer need to invest in hardware and maintain it throughout its lifetime. However, choosing the right GPU service from the sea of options is not easy. Many providers offer GPU services on demand but differ substantially in terms of performance, cost, maintenance, support, and many other aspects. In this blog post, we review the top 10 cloud graphics processing unit platforms for AI and massive workloads across different use cases like Gaming


What is a Cloud Graphics Processing Unit Platform?


A cloud GPU platform provides virtualized hardware environment for running applications and performing compute-intensive tasks. It also offers dedicated resources for specific tasks, thus optimizing the utilization of the underlying hardware. Cloud GPU platforms are a great fit for data-intensive workloads where the cost of ownership and maintenance outweigh the benefits of owning the hardware. You can attach GPUs to your virtual machines, containers, or dedicated servers with a cloud GPU platform. Depending on the specific service, you can choose the number of GPUs, the type of GPUs, the amount of memory, and the number of CPUs. Cloud GPU platforms also provide various software tools and libraries to simplify the process of setting up and running GPU-enabled applications.


Graphics Processing


Graphics processing units (GPUs) are designed to handle parallel computing tasks such as image and graphic processing, machine learning and other data-intensive operations. Traditionally, GPUs have been used in high-end computer systems such as gaming consoles, high-performance computing systems, and computer workstations. Cloud providers have started offering GPU services to deliver high-performance applications and machine learning. Using GPUs in the cloud allows businesses to scale up, or down their compute capacity as needed and pay only for the resources that are actually utilized.


GPU for AI


Artificial intelligence (AI) is about building and training intelligent systems that can simulate human behavior and help businesses optimize their operations. AI offers great potential for developing new products and services that can transform the way we live and work. GPUs are used for training artificial neural networks for machine learning. They facilitate parallel computing processes that are required to train these networks. Parallel computing is the simultaneous execution of processes by dividing the work into smaller chunks and distributing them among different computing nodes. However, not all GPUs are created equal. It would be best if you choose a GPU that is optimized for training to maximize the performance of your neural networks. Nvidia and Intel provide specific GPU boards for machine learning and AI.


How GPU Works


GPUs are made up of thousands of parallel computing units that are designed to handle compute-intensive tasks such as computer graphics, image processing and artificial intelligence training. In general, each parallel computing unit processes a single operation, and then the results are collected and recombined to generate the final result. The parallelism in GPUs is achieved using a " streaming technique," which allows the data to be transferred from the memory to the GPU in one continuous flow.


How to Select the Right Cloud GPU Platform for Your Business


Here is a checklist to select the right cloud GPU platform for your business. You can decide which one fits your needs the best based on these parameters.



● Performance: The performance of the GPU platform should be guaranteed. You should be able to scale your application resources based on demand. You should also be able to switch between different GPU types for different workloads.
● Cost: The cost of cloud GPU services changes often, so check the latest prices and compare them across different providers.
● Maintenance: It's important to understand the maintenance model of the GPU platform. Some providers charge based on the number of hours used, while others offer an on-demand model. Some models also charge per gigabyte of data transferred.
● Support: Make sure the vendor offers 24x7 support. You should be able to quickly reach out to their engineers and get detailed answers to your queries.
● Security: Cloud GPU platforms should implement security measures to protect data in transit and at rest. They should also have transparent data encryption policies to keep your data confidential.

GPU Use Cases


Computer Graphics Animation and Rendering


Computer graphics animation and rendering are used in the film, gaming, and architecture industries. Architects rely on computer graphics to create architectural renderings and visual simulations. Graphics artists use computer animation to create visual effects and digital movies. You can use a cloud GPU platform to render massive architectural visualizations and 3D architectural walkthroughs quickly. Architects and designers can share these visualizations with their clients and stakeholders to get early feedback on their projects. Depending on the complexity of the architectural visualizations, it can take an average of three to six hours to render a single scene on a CPU-only virtual machine. Using a cloud GPU platform for rendering can reduce the rendering time to minutes.


Machine Learning and AI


If your business is developing or deploying applications that use machine learning and AI technologies, you should use a GPU-enabled public cloud service for training your models. This way, you don't need to invest in hardware and manage it throughout its lifetime. Cloud GPU services offer great flexibility and scalability to host large-scale machine learning and AI training environments. You can use them for training even the most computationally-intensive neural networks. However, not all GPUs are created equal. You need to choose a GPU platform that is optimized for AI and machine learning applications.


Video Editing, Transcoding and Backup


If you are running a video editing or media transcoding operation, you can use a GPU-enabled cloud service to speed up your workflow. Some cloud providers also offer backup and disaster recovery services using GPUs. You can use a GPU-enabled cloud platform to store and protect your data by running real-time backups.


Bottom Line


Choosing the right GPU platform for your workload can be a daunting task. You can expedite this process by following the checklist outlined in this blog. Make sure to consider the performance, cost, maintenance, support and security aspects of the cloud GPU platform before selecting one for your business.

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