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4 Considerations for GPU Server Adoption in the Enterprise

Elice

2024/3/28

As we enter the era of generative AI, so does the importance of GPUs. With over 80% of the GPU market for AI, Nvidia’s stock price has risen more than 230% in just over a year, it’s clear to see the role and importance of GPUs. In this content, we’ll take a closer look at why GPUs are gaining so much traction, and dive into the key considerations organizations need to make when adopting GPU servers for AI training and data analytics.


What is a GPU? From Concepts to Examples! - Jump to


GPU, CPU, and NPU differences

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A Graphics Processing Unit (GPU) is a computing device that specializes in parallel processing. We’ve covered the concept in detail in past content, but today we’ll summarize the differences between CPUs, GPUs, and NPUs.

CPUs, GPUs, and NPUs each have specialized areas of focus. CPUs are general-purpose processing units that can perform a wide range of tasks, while GPUs specialize in graphics processing, making them great for high-performance computing tasks. Similar to GPUs, NPUs are also useful for high-performance computing tasks. However, NPUs are more optimized for AI training and research and deep learning tasks than GPUs, making them much less general-purpose. Here’s how CPUs, GPUs, and NPUs compare in terms of versatility, performance, and power efficiency.


  1. General Purpose: CPU > GPU > NPU

  2. Performance (based on deep learning): NPU > GPU > CPU

  3. Power efficiency (for deep learning): NPU > GPU > CPU


GPU Server Use Cases

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GPU servers are virtualized compute services that can be allocated and use GPU infrastructure resources. GPUs perform parallel computations with thousands of cores to quickly process large amounts of repetitive, similar operations. Originally used for graphics tasks such as gaming and design, they are now being used in a variety of fields, including deep learning, AI, and data analytics.


When companies want to utilize GPU infrastructure, they often choose to allocate it through a cloud service rather than build their own servers. This is because using a well-organized cloud service is a way to reduce upfront costs and quickly utilize resources while maintaining high reliability, rather than purchasing expensive GPUs and managing the servers themselves. GPU server clouds enable companies to perform a variety of tasks, including deep learning, AI training and research, as well as graphics processing, simulation, data analytics, machine learning, and more, which can accelerate enterprise innovation.



1. Do I have a modern GPU?

First, you need to make sure you have a modern GPU to perform deep learning and other high-performance tasks more efficiently. With recent advances in technology, new algorithms and models are constantly emerging, and the latest GPUs offer higher performance and efficiency, resulting in faster and more accurate results. At Elice GPU Cloud Services, we’ve built the highest-performing GPU server farms to provide world-class infrastructure.


2. Is a GPU server available at a reasonable cost?

Next, you need to consider whether you can utilize the GPU cloud service at a reasonable cost, especially the pricing plan that allows you to allocate resources flexibly. Elice GPU Cloud Service offers Basic and Pro plans to help you pay for the service according to your organization’s needs, so you can compare different conditions and make the service more affordable.


3. Is reliable GPU server allocation possible?

Third, it is important to have stable server allocation, because if the GPU server suddenly stops or degrades in the middle of a job, it will result in wasted resources such as data loss and increased work time. Elice GPU Cloud Service supports ultra-high-performance and stable connections with server farms located in Korea, which provides many advantages such as stability, low latency, data transfer speed, and sensitive data sovereignty regulations.


4. Are there additional services that maximize GPU server utilization?

Finally, it’s worth considering whether there are additional services that can maximize the utilization of GPU servers. For example, if you want to use GPUs for AI education and research, you may want to use a service that provides such an environment immediately. Elice AI Dedicated GPU Cloud Service provides a variety of environments for AI education, research, and service development. In conjunction with Elice LXP, it provides an environment for AI education, PBL, coding tests, and competitions based on ultra-high capacity data.

When selecting a GPU server, it is recommended to consider (1) performance, (2) cost, (3) stability, and (4) features and technical support. Elice GPU servers fulfill all of these criteria, and are recommended for organizations that are looking to achieve enterprise-wide DX, such as AI training and data analytics.

Having already provided GPU clouds for AI training, research, and DX activities for many organizations and enterprises, Elsevier also offers customized GPU solutions based on its know-how. If you’re looking to customize and utilize the latest and greatest GPUs at an affordable price, Elice is the company for you. We’ve covered the differences between GPUs, CPUs, and NPUs, use cases for GPU servers, and four considerations when selecting a server. We hope this content helps you better understand which GPU server is right for your organization.


The Right GPU Solution for Your Organization - Learn More About Elice Cloud


*Elice owns the copyright to this content, which is protected under copyright law.

*Without prior consent, secondary processing and commercial use of the content are prohibited.

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  • #Cloud

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