Compute-intensive workloads like machine learning and artificial intelligence are becoming common in many industries, with businesses using these technologies in various ways. Many of these workloads are run on GPUs that are better at handling computational and data-intensive tasks, meaning these pieces of hardware are in high demand.
Businesses have two options to choose from when deciding where to source GPU computer power: cloud-based GPU instances and on-premises or traditional GPU options. While they are very similar, there are some things you should know when picking between these options.
What are Traditional or On-premises GPUs?
They are simply GPUs that you install on your on-premises server(s). They are comparable to the GPU options you would find on personal computers, even though they have additional features that make them perfect for the workloads they handle.
Cloud GPUs
Cloud GPUs are installed on servers provided by providers or other businesses. The available graphics processing units (GPU) cloud solutions from companies like Gcore are often installed on bare metal servers so you have all the performance and capabilities the server affords you.
These GPUs are often in the same class as those used on on-premises servers, with the difference being that they are owned and managed by a different entity.
The Benefits of On-premises GPUs
The main benefit of on-premises GPUs is that you have direct control over them. Since they are installed on your premises, you can do anything with them. With complete control over the hardware and software, you can customize and optimize them deeply to ensure they can handle the types of workloads you want them to.
On-premises GPUs have a much lower latency compared to their cloud counterparts, which is especially true for tasks that require little to no network communication. This lower latency is due to the physical proximity of the hardware to where it is needed.
On-premises GPUs are also a much better option for handling sensitive data. Businesses collect different types of data, some of which are sensitive. Businesses that do this might consider keeping all their data and its processing on-premises to avoid exposure to the outside world.
On-premises GPU solutions are, therefore, the better option in these cases. They are an even better option for these use cases if they are not connected to a network.
The Downsides of On-premises GPUs
GPUs, especially those used for heavy workloads, are expensive. They sometimes cost tens of thousands of dollars, which is a cost many businesses cannot afford. For this reason, those that need their computational power have to deal with a significant upfront investment.
Their cost is also the reason many businesses use older GPUs. Replacing one workload GPU might not be a challenge for businesses making the most of it because that is an expense the owner or leadership can deal with. However, things become more challenging for businesses that own multiple compute GPUs.
In addition to sticking with older models, it is common for businesses that go with this option to have a mismatched GPU lineup. They may have some new models and older ones depending on their upgrade cycles. This can mean a difference in performance across their IT infrastructure.
On-premises GPUs also provide limited scaling. Adding and removing hardware is a physical task and this can hinder scalability for businesses that handle fluctuating workloads.
The Benefits of Cloud GPUs
Businesses that provide graphics processing units (GPU) cloud solutions provide unprecedented flexibility compared to on-premises GPUs. You can scale your needs up and down as you wish, which can help you get the most out of these solutions while keeping your costs low.
This helps solve a crucial downside of on-premises GPUs: over-provisioning and unused resources. When choosing an on-premise GPU solution, many businesses do so with the future in mind. Because of this, they often end up with hardware that they cannot take full advantage of. They then have to deal with the costs associated with underutilized hardware whose full capabilities they take advantage of once in a while.
Businesses do not have to deal with the upfront cost of buying the GPUs while also benefiting from a pay-as-you-go payment model. This ties into the overprovisioning issue discussed above.
Cloud GPUs also provide much better flexibility. Team members and employees can access the server resources from anywhere in the world as long as they have an internet connection and the correct authorizations. For this reason, cloud GPU solutions easily facilitate collaboration among dispersed teams.
Lastly, they reduce maintenance and upgrade burdens. Cloud GPU solutions are managed, meaning another company and its IT professionals are responsible for maintaining them.
The business does not have to patch or update any software, and the provider will swap existing GPUs for new ones once available. However, note that the provider can increase the cost of their services when they upgrade to new hardware to make a return on their investment.
The Downsides of Cloud GPUs
Despite their providers’ best efforts, these GPUs introduce some network latency. While this might not be a big deal if you use them for tasks like data analysis, it is a serious consideration for businesses that handle real-time processing or data ingestion.
Businesses also have to deal with security considerations when they choose this option. They must ensure that all sensitive data they send to the cloud and process using these GPUs remains safe at all times. This means using these solutions requires careful planning and significant security measures from both the cloud provider and the user.
About Hybrid Solutions
A growing trend is businesses opting for a hybrid solution; a combination of on-premises and cloud solutions. Doing so allows them to leverage the upsides of both solutions while avoiding their downsides. For example, they can use on-premises GPUs for core workloads and sensitive data handling and use cloud GPUs for bursts in demand or experimental projects.
On-premises and cloud GPUs are the modern go-to options for handling demanding computational workloads. Understanding their pros and cons will help you choose the most suitable option for your business and use case. It will ensure you get the power you need without it costing you too much.
