When it comes to building and running AI models, one of the first questions to answer is where to put your graphics processing units (GPUs): Should they live on-premise in your own data center, or should you tap into the cloud? On-premise GPUs give you speed, control, and the comfort of keeping sensitive data in-house, while cloud GPUs make it easy to scale up quickly without a big upfront investment.
The choice comes down to how you train and use your models, what kind of data you’re working with, and how flexible you need your AI infrastructure to be. This article covers the differences between an on-premise vs. cloud GPU, the benefits of each deployment model, potential challenges, and what factors to consider in your final decision.
Key takeaways:
On-premise GPUs can provide low latency, customizable infrastructure configurations, and meet compliance and security requirements for regulated industries.
Cloud GPUs offer greater flexibility, pay-as-you-go pricing models, and more global availability for AI model training and SaaS hosting.
The GPU you select will depend on your organization’s requirements for performance, scalability, infrastructure management, and cost.
An on-premise GPU is a physical GPU that resides in an on-site data center server. It is primarily used for use cases with low latency requirements or specific data sovereignty needs. Top AI use cases for on-premise GPUs include healthcare medical imaging, fraud detection, predictive maintenance, robotics and automation, and high-performance computing research models.
Meanwhile, cloud GPUs have high-performance processors hosted in the cloud. You typically access these resources through web interfaces, APIs, or command-line tools, and pay on a usage basis—either per hour of compute time or through subscription models—allowing you to scale costs with your actual needs. Cloud GPU services are offered by companies like DigitalOcean, AWS, Google Cloud, and Microsoft Azure. These GPUs work well for dynamic workloads, rapid scaling, and parallelized computing. They can support AI model training at scale, AI-based SaaS, MLOps, and AI experimentation.
Each GPU type brings its own set of benefits for AI/ML workloads. On-premise GPUs can provide minimal latency, increased infrastructure customization, long-term ROI, and compliance for specific regulated industries. Cloud GPUs are more scalable, cost-effective for short-term projects, and remove technical overhead and configuration.
On-premise GPUs can provide increased performance, especially with single-tenant setups in dedicated data centers, but they can be tougher to scale in real time or easily provision new GPUs. Cloud GPUs can scale in real time or even autoscale resources, but performance will depend on network bandwidth and load. Yet most cloud providers provide near-native speeds.
Minimal latency: Installing on-premises GPUs and using in-house infrastructure means you can have custom network setups that can minimize latency and support use cases with high throughput and tight latency requirements. The GPU hardware is also physically closer to any on-premises data stores or sources, which can help improve overall latency by keeping processing close to the initial source.
Full infrastructure control: Having GPUs in your own data centers and as part of your infrastructure means you have complete access to them and can more easily configure them to run specific applications or operating systems or integrate them into in-house-specific tech stacks if you choose. This also allows you to implement any proprietary or required configurations or workloads if necessary.
Security and compliance: For more heavily regulated industries, such as healthcare, finance, or government, having on-premises GPUs and infrastructure can bring extra security, as all hardware can stay on a private organizational network or within a specific data center. This setup can reduce the potential attack surface for security breaches and ensure industry regulation compliance.
Cost over time: On-premise GPUs can require a large upfront financial and time investment, but depending on the characteristics of your workloads, they can be more cost-effective as you continue to use the same GPUs within your own infrastructure and spread the cost over months or years. This is ideal for long-term investment in GPU hardware or organizations that have a stable need for GPU computing power.
Increased flexibility: Cloud GPU resources are designed to easily scale up or down as required. This makes them ideal for short bursts of high-performance computing or projects with elastic workloads and processing requirements. With their scalability, these GPUs can match processing requirements in real time and reduce the amount of idle or overprovisioned infrastructure over time.
Global availability and reach: Service providers can provide cloud GPUs for multiple regions and availability zones. This means that your organization isn’t tied to one specific data center when it comes to getting GPU resources and accessing cloud GPUs across multiple data centers. This can help you select data centers that can provide increased performance and reduced latency for your applications.
Cost-effectiveness: Cloud GPUs often use pay-as-you-go pricing models, which make it easier to get computing power at a much more flexible price range than buying on-premise GPUs. When you pay for cloud GPUs, simply pay for the computing power that you need at rates specified by the cloud provider.
Reduced operational costs and overhead: Cloud GPU providers manage all the infrastructure associated with running GPUs. This means your internal IT department doesn’t have to spend time maintaining servers, updating firmware, or troubleshooting hardware when incidents occur, and these associated provisioning and maintenance costs aren’t part of your budget.
Are you curious about where to start with cloud GPU platforms for AI/ML? Here’s a curated list of options from DigitalOcean.
Both GPU types have constraints around performance, potential hidden costs, security concerns, and difficulties around capacity planning.
Need tips for GPU cost optimization? Here are seven ways to cut costs and still maintain performance.
Sustained performance over time: Even with direct access to GPU computing power, performance can degrade over time without consistent maintenance, hardware optimization, and effective cooling infrastructure to support compute-intensive AI use cases.
Hidden data center costs: In addition to the large initial upfront costs associated with purchasing on-prem GPUs and configuring and provisioning the data center, additional costs exist for power, cooling, and technical staff to keep everything online and running. If you decide to upgrade GPU hardware or increase your data center footprint, you will also incur additional costs.
Limited scalability: Scaling your infrastructure for future capacity with on-premise GPUs requires extensive planning to determine how much new infrastructure to invest in and provision. Once you decide how many GPUs you need, you must purchase, install, and configure the hardware, which incurs cost and requires time.
Physical security and single point of failure: Having on-premise GPUs also requires physical infrastructure security for your data center to ensure the building, server racks, and network are secure. Beyond physical security, you must have redundancy and backup measures in place to avoid a single point of failure should specific GPUs or data center infrastructure fail.
GPU availability and quotas: Cloud GPUs can slow down performance with very large AI datasets or models, depending on cloud provider resource quotas and GPU availability. Additionally, cloud GPUs may only be available in specific data centers, which could affect performance depending on where you run your regional workloads.
Additional cloud provider costs: Cloud GPUs allow you to just pay for the computing power that you use. However, depending on the cloud provider, you may find yourself paying for data ingress and egress costs, additional management services, and add-on tools. You must also track your GPU usage to avoid unexpected usage spikes and resource overprovisioning.
AI workload scalability: Cloud GPUs can provide more scalability, but depending on cloud provider quotas and regional availability, scaling AI workloads in the cloud can be difficult. Additionally, scaling AI workloads in the cloud can lead to unexpected costs or unnecessarily provisioning GPU resources.
Additional security requirements: Cloud GPUs still require you to configure firewalls, access management policies, and monitoring tools, as the cloud does have a wider attack surface than on-premise data centers. These offerings may or may not come from your cloud provider, which would require additional software and time investment to secure your infrastructure.
Ultimately, deciding on an on-premise GPU vs. cloud GPU for your AI applications and use cases depends on the specifics of your workloads, where your data primarily resides, internal performance requirements, available budget, and any necessary industry requirements. Here’s a look at the main factors to consider:
Usage patterns: How often do you require GPU computing resources? How many GPUs do you require when you run specific workloads? For more sustained, stable workloads, it makes more sense to rely on on-premise GPUs. Dynamic workloads benefit more from cloud GPUs that can easily scale to match real-time processing demands, especially if those requirements change fairly quickly.
Performance requirements: On-premise GPU setups can give you and your team more control over network configurations, allow you to customize your GPU setups to meet specific performance requirements, and optimize your hardware. Cloud GPUs can provide near-native speeds and easy access to the latest GPUs like NVIDIA H200s, which can increase performance.
Scalability: Consider how often you’re adding more GPUs to run workloads. On-premise GPUs offer scalability if you plan on needing extra capacity over time and have the available number of GPUs in your data center. Cloud GPUs can easily scale up and down as needed.
Resource availability: On-premise GPUs and cloud GPUs have different availability models. With on-premise GPUs, you have full access to your GPUs in a dedicated data center, but you also have the liability of maintaining those resources and meeting internal service level objectives. Cloud GPU availability can vary across cloud provider data centers, but you are not responsible for infrastructure maintenance and upkeep.
Cost: Budget can sometimes be the deciding factor in GPU selection. On-premises GPUs require a substantial upfront investment and then paying staff to help maintain and run your data center. Cloud GPUs don’t require initial capital, but can sustain more operational costs over time depending on how many cloud GPUs your workloads use and the cloud provider’s billing structure.
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What happens if my on-premise GPUs fail vs cloud GPUs?
An on-premise GPU failure can cause task disruption, cluster instability, driver issues, and hardware connectivity problems. Cloud GPU failure may result in memory errors, limited or non-existent resource availability, crashed workloads, increased latency, or lost workloads.
How do I optimize GPU usage for cost efficiency?
Cost optimization tasks for GPUs include knowing when to use a CPU vs. GPU, using spot instances and preemptible VMs, rightsizing GPU instances, investigating the use of multi-instance GPUs, monitoring overall GPU use, and working directly with providers to negotiate desired costs or discounts.
Should I consider alternatives like TPUs or custom chips?
Whether or not you should use a TPU or a custom chip instead of a GPU will depend on what AI tasks and workloads you want to run. TPUs can help with tasks such as natural language processing, image recognition, and recommendation systems. Whether or not it will bring cost savings or performance improvements depends on the specific model and any necessary requirements. GPUs are more suited for a broader range of AI/ML applications, which may be beneficial for you and your team.
Are cloud GPUs cheaper than buying on-premise GPUs?
Cloud GPUs are typically cheaper for short-term or intermittent workloads since you only pay for what you use. For sustained, heavy usage over months or years, on-premise GPUs can be more cost-effective despite the large upfront investment.
How to choose between cloud and on-premise GPU?
Choose on-premise GPUs if you have sustained, stable workloads, need maximum control over performance and security, and can justify the large upfront investment. Choose cloud GPUs if you have variable or intermittent workloads, need to scale quickly, want access to the latest hardware without maintenance overhead, or prefer lower upfront costs.
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Key features:
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