In the past, a business did not have to think about the type of server to get. Most businesses’ needs could be satisfied with a server with enough computer power, RAM, and storage. However, things have changed rapidly in the last few years, with businesses seeking servers for specialized workloads. Some of these include machine learning and AI model training.
Such applications require servers with enough GPU computing power, leading to businesses needing to consider different factors than a business would a few years ago. In this article, we will look at several factors you should consider when choosing a GPU server for your business or other needs.
The Characteristics of Your Typical Workloads
Each business has different systems, processes, and workloads. This is why workload characteristics are among the first things to consider when choosing a GPU server. Some may need a server capable of handling scientific simulations, while others may need one for analyzing financial data, 3D rendering, video editing, or even deep learning.
Each of these workloads has different requirements, specifically the required memory amount, computer power, and interconnectivity.
When assessing workload characteristics, it is also crucial to consider data characteristics. These include the complexity, size, and format of the data. A business that processes many images and videos has different data characteristics than one working with large worksheets and datasets.
Data characteristics also influence how much power you would need for batch or real-time processing.
Lastly, businesses must consider crucial performance requirements. Some workloads require very low latency and interference, some require high throughput, and others have low time-to-solution metrics (the time it takes to process input data and get the required solution).
The best way to approach this is by deciding the minimum GPU performance you need to meet your requirements. After that, you can seek solutions from providers that meet these minimum requirements while allowing you to scale as and when you wish.
GPU Architecture and Specifications
Once you understand your workload and computing requirements, you can start evaluating different GPU server options and their technical specifications. Remember that different GPUs have varying specifications that may or may not make them ideal for your workloads.
Selecting the appropriate GPU model is crucial for getting the computing power you need. For example, getting NVIDIA A100 powered servers is a much better proposition for businesses handling anything to do with machine learning and artificial intelligence.
It is also a good idea to ask providers about the characteristics of the different GPUs they offer. For example, they may tell you that the NVIDIA A100 and H1OO GPUs are similar but have differences that make one better.
Remember to keep other factors in mind that affect the final GPU cost and compatibility with your software stack, such as power efficiency.
As you evaluate the different GPU models, remember to look at their memory capacity. Workloads like machine learning model training and image compression require that you work with large amounts of data.
For this reason, you should have enough GPU memory to hold whatever you are working with. This reduces the amount of data swapping that has to happen between the RAM and storage, reducing latency and increasing data processing rates.
If your workload requires parallel computing, you should also understand how many CUDA cores or stream processors the GPUs in your chosen server have. NVIDIA introduced CUDA cores to its computer GPUs for better computing performance, so the more you have and the more powerful they are, the better the performance.
Consider Your Computing and Storage Requirements
Even though the main star of the show for a GPU server is the GPU, it still needs a CPU that can keep up and enough storage for all the data you will be dealing with.
Assess the CPU properly to ensure it is sufficient. Specifically, look at the number of cores, cache size, and clock speed to determine if it fits your needs. If you do not know how to assess this, look at independent reviews and research the different terms used.
In many cases, it is not a good idea to ask a vendor if the CPU they provide is the best. Why? It’s their job to get you to get a server from them, so they might not always give you the best information or tell you that their CPUs are not the best.
Getting a GPU server makes it plausible that you also need fast data read and write speeds. In addition to how much raw space you need in terabytes or petabytes, you also need to know the type of storage a vendor or provider uses.
At the very least, they should use fast SSDs. However, go with NVMe if you need faster speeds. Just know that the latter option might be more expensive, even though some vendors price both options similarly.
Power and Cooling Capabilities
Most businesses will be perfectly happy with leasing or getting a server from a provider. The main advantage of this option is that you do not have to worry about server needs like power and cooling. However, you will always rely on the company to handle hardware or software upgrades and patches.
For these and other reasons, it might make sense to build your own. If you decide to do so, you must keep power and cooling considerations in mind. The good news is that modern GPUs like the NVIDIA A100 and H100 are very efficient, so they do not consume too much power.
However, you still have to cool them, which is a crucial consideration if you run a server with numerous GPUs. You might also need to consider the need for specialized cooling solutions (e.g., liquid cooling, high-performance air cooling) to maintain optimal operating temperatures.
Scaling and Expansion
If you are worried about future expansion and scaling, it is best to choose dedicated GPU servers from reputable providers like Gcore. The reason is that their servers scale up and down depending on your needs, so you only pay for what you need.
However, you might need your GPUs to run all the time, with your expanding workload requiring more physical GPUs. If you are in this situation, and depending on your long-term growth plans, you may want to consider the scalability and expansion capabilities of the GPU server.
Start by evaluating the server’s ability to accommodate additional GPUs, CPUs, memory, or storage as your workload demands increase over time. You should also assess the server’s support for GPU server clustering and integration with distributed computing frameworks. Both enable large-scale parallelism for your workloads and might be the solution you are looking for.
Considering the factors above, as well as the cost of ownership and acquisition, it should be much easier to select the GPU server that best aligns with your workload requirements. Such a server should also ensure optimal performance, efficiency, and cost-effectiveness for your compute-intensive applications and business.
