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.

Infrastructure Requirements

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.

Cost Analysis

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.

• Data Center Costs: Running a cluster requires a climate-controlled environment that has good power and is physically secure, which will increase the cost of space, electricity, and cooling.
• Hardware Costs: High-efficient GPUs are expensive; the cost of the multiple servers, networking bills, and storage requirements that will grow will drive the bill very high.

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.

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.

Benefits of Cloud-Based GPU Solutions

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:

• Software updates and licensing: cloud providers handle the stress of licensing and updates for both software and security.
• Scalability: Cloud service providers allow scaling, which means users can increase or decrease the resource based on the demand. This is important for projects with different demands
• Maintenance: The cloud providers handle infrastructure, hardware repairs, and updates, which reduces the in-house IT workload.

Efficiency and Resource Utilization

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. 

Cost and Performance Comparison Example on RunPod

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.

Common Misconceptions about Cloud-Based GPU Solutions.

Regardless of the advantages, some misconceptions about cloud GPU solutions continue.

• Cloud is too expensive in the long term. While cloud costs can increase over time, they often remain lower than on-prem setups when considering hardware depreciation, operational costs, and maintenance can be more expensive. As shown above, the hardware can be quite expensive, and data center and talent costs for maintenance continue indefinitely; even if you reach a breakpoint where the hardware is "paid off" with enough use, data center and expertise Cloud-based GPU solutions offer flexibility, which allows organizations to shut down resources when they are not in use.
• Performance might not be stable, but some providers offer high-performance dedicated instances that are designed for consistent workloads. Cloud infrastructure offers equal or better performance stability than self-managed clusters.
• Data and security concerns: Cloud providers know about the importance of the data security of their customers and comply with standards higher than what many organizations can achieve. Most of them use encryption, multi-layered security, and network isolation, which ensures a safe environment compared to in-house data protection measures.

Considerations for choosing between cloud and on-premises solution

While cloud GPUs are altogether better, organizations should consider workloads specifically when choosing:

• Workload Duration and Type: Temporary or flexible workloads are better in the cloud, while long-term continuous workflows may benefit from on-prem clusters.
• Budget: Companies with limited budgets benefit from the cloud’s reduced management needs; however, those with dedicated data center staff and capital may justify on-premise hardware.
• Scalability requirements: If growth predictions involve significant scaling, then cloud solutions would enable rapid expansion without additional investment, allowing organizations to match infrastructure growth to demand easily.

A Real Life Case Study: Some Hard Numbers

Executive Summary

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.

Scenario

• Workload: Training large language models and computer vision models
• Required capacity: 4x NVIDIA A100 GPUs (80GB)
• Usage pattern: 70% average utilization
• Storage requirement: 1TB
• Time period: 3 years

On-Premises Costs

Initial Hardware Costs

• 4x NVIDIA A100 GPUs: $10,000 × 4 = $40,000
• Server chassis and CPU: $15,000
• Networking equipment: $5,000
• Total Hardware: $60,000

Infrastructure Costs (Annual)

• Data center space: $12,000/year
• Power consumption (4 GPUs + server):
  • 1,400W × $0.12/kWh × 24h × 365 days = $1,472/year
• Cooling costs: ~50% of power = $736/year
• Annual Infrastructure: $14,208

Operating Costs (Annual)

• System administrator (part-time): $40,000
• Maintenance and repairs: $5,000
• Software licenses: $3,000
• Annual Operating: $48,000

3-Year Total On-Premises Cost

• Initial hardware: $60,000
• Infrastructure (3 years): $42,624
• Operating (3 years): $144,000
• Total 3-Year TCO: $246,624

Our Cloud Solution Costs

Compute Costs

• Hourly rate for 4x A100 GPUs: $6.56
• Annual hours at 70% utilization: 6,132 hours
• Annual compute cost: $40,226
• 3-Year Compute Cost: $120,678

Storage Costs

• Storage rate: $0.05 per GB per month
• Storage requirement: 1TB (1,000 GB)
• Monthly storage cost: 1,000 GB × $0.05 = $50
• Annual storage cost: $600
• 3-Year Storage Cost: $1,800

3-Year Total Cloud Cost

• Compute costs: $120,678
• Storage costs: $1,800
• Total 3-Year Cloud TCO: $122,478

Cost Comparison and Benefits

Direct Cost Savings

• 3-Year On-Premises TCO: $246,624
• 3-Year Cloud TCO: $122,478
• Net Savings: $124,146 (50.3%)

ROI Analysis

• Avoided upfront capital expenditure: $60,000
• Eliminated operational overhead costs
• Faster project deployment: 8-10 weeks saved
• Estimated First-Year ROI: 95%

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.

Conclusion

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.