Machine learning, AI, and data science workloads rely on powerful GPUs to run effectively, so organizations are deciding to either invest in on-prem GPU clusters or use cloud-based GPU solutions like Runpod. This article will show considerations of infrastructure requirements and compare the cost and performance to help you choose which solution is more scalable, cost-effective, and efficient for you.
For AI and machine learning workflows to scale, they need computational power, memory, and the ability to handle resource requirements, which GPUs can handle. High-end GPUs that meet the memory requirements for data processing and the capability to scale according to the intensity of the workloads. For organizations and teams considering on-premises infrastructure, the building and maintenance of this setup require significant investments in power, cooling, hardware, data centers, and security.
Cloud providers make the process simple by offering ready-made GPU instances that let you bypass the extensive requirements to set up on-prem clusters. Users can easily provision the computing resources they need, thereby eliminating the need for physical infrastructure and maintenance. This allows teams to focus on their core projects without worrying about the infrastructure, and a cloud GPU provider focuses on scaling the workload demands of their projects.
The budget to set up an on-premises GPU cluster is high because you have to account for servers, storage, networking tools, GPU hardware, and equipment for data center management.
On the other hand, cloud GPU providers like Runpod operate a pay-as-you-go model, which breaks the financial entry by letting you pay for what you use as you use it. Some organizations do not need these GPUs to run 24/7, so this is perfect because your costs are according to your usage, so there are no unused resources. Below is a table comparing the costs of on-prem and cloud.
| Cost Factor | On-Premises GPU Cluster | Cloud GPU |
|---|---|---|
| Initial Hardware Investment | High upfront costs for GPUs, servers, and networking | No initial investment, pay-as-you-go |
| Infrastructure Setup | Requires data center space, power, and cooling | No data center costs, resources managed |
| Staffing Costs | Dedicated IT staff for maintenance and monitoring | Minimal IT staff required |
| Maintenance & Upgrades | Regular hardware replacements and software updates | Managed by Cloud provider at no extra cost |
| Operational Costs | Fixed monthly power, cooling, and space expenses | Variable, based on usage hours |
| Flexibility & Scalability | Limited by physical infrastructure | Easily scalable, flexible resource allocation |
| Monthly Cost Estimate | High (fixed costs, regardless of usage) | Variable (based on active usage only) |
To put it in perspective, a single H100 can cost up to $25,000 just for the card itself, and that's before the cost of the machine around it, data center amenities like cooling, data linkups, and hosting, as well as the expertise required to pay for its operation and maintenance, whereas you could rent that same H100 on Runpod for tens of thousands of hours and still not yet be at your break even point. This puts even the most expensive hardware right into your reach even for the smallest projects.
A major benefit of using cloud-based GPUs is that there's no need for upfront capital; unlike traditional clusters, Runpod and similar services do not require any upfront monetary commitment from organizations. Also:
Low utilization happens with on-prem clusters due to off-peak periods, as resources are idle when not used actively, unlike cloud providers that manage resources efficiently, and you can use resources when you need them and prevent waste. Users can pay for active usage, making them best for temporal projects and unpredictable workloads.
There are various needs for GPUs, such as temporal data analysis, machine learning, scaling, or inference for productions, so cloud solutions will be the best to manage efficiency. This ensures no overhead cost for underutilized hardware, and you pay for what you use.
Let’s consider a real-world example comparing a project that runs an on-prem GPU cluster and a project deployed on Runpod. The on-prem cluster will require a multi-week setup period for the procurement, installation, and testing of hardware. But with Runpod, the setup process could be completed in hours by provisioning pre-configured instances.
Runpod’s pricing model makes cost-saving easy. Except for a dedicated cluster that incurs fixed monthly costs regardless of usage, users only pay for runtime. Performance benchmarks show efficiency; with Runpod’s cloud instance, you can achieve similar or superior processing speeds, high reliability, and performance metrics that meet standard demands without overhead management.
Regardless of the advantages, some misconceptions about cloud GPU solutions continue.
While cloud GPUs are altogether better, organizations should consider workloads specifically when choosing:
This analysis compares the total cost of ownership over a 3-year period for a machine learning workload requiring 4 NVIDIA A100 GPUs, comparing on-premises deployment versus our cloud-based solution.
Our cloud solution offers an exceptional 50.3% cost saving over three years compared to on-premises deployment, representing over $124,000 in direct savings.
Note that we also offer Savings Plans, which can provide a significant percent-based cost savings with a minimum term of a 30 day rental, so for heavily utilized GPUs this can provide even further savings.
The benefits of cloud-based GPU solutions are they reduce upfront costs, ease of management, scalability, and proper resource utilization. By providing GPU power without the monetary issues of running an on-premises cluster, organizations can save, particularly for changing workloads with cloud providers. Cloud-based GPUs focus on infrastructure management and let organizations focus on their computational goals for businesses looking to enhance AI and machine learning capabilities with minimal investments.
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