Running AI Workloads: Renting Cloud GPUs vs. On-Premise Hardware

Training and deploying AI models is no small feat. High-performance GPUs, massive datasets and long compute hours are all part of the equation. To give an idea, earlier this meant investing heavily in on-premises infrastructure, which included racks of GPUs, storage servers, networking, cooling systems and different teams to maintain it all.

But not anymore.

Today, you can get the same power and performance through cloud GPUs at scale without spending months (or millions) building and maintaining your own infrastructure. Cloud GPU providers have democratised access to advanced compute power, letting individuals, startups and enterprises build and scale AI systems on demand.

In this blog, we’ll explore why running AI workloads with cloud GPUs is a smarter choice than on-premise hardware.

What is the Problem with On-Premise GPU Hardware

Before we talk about the problems, it’s worth understanding why on-premise hardware is not always practical, even for large teams.

Running AI workloads in-house requires a significant upfront investment. You’re not just buying GPUs as you’re building an entire ecosystem around them. That includes power systems, storage, cooling, networking, rack space and skilled engineers to manage it all. For example, a single NVIDIA H100 GPU can cost over $25,000 and serious training workloads often require clusters of them. For example, Meta used 24,576 NVIDIA Tensor Core H100 GPUs to train one of its most advanced AI models Llama 3.  

However, it is important to note that leading companies like Meta can afford such scale but this is not the case with startups, individuals or even some enterprises. Because when your projects grow, adding capacity means purchasing more hardware, waiting for delivery and reconfiguring systems, all of which slows down innovation. On the other hand, when your workloads drop, those expensive GPUs sit idle and eat up power and capital with no return.

And let’s not forget maintenance. Hardware fails, firmware needs updates and data centres require physical attention. This operational overhead can drain your time and focus away from what actually matters: building and launching great AI models.

Why Cloud GPUs are a Smarter Choice

Cloud GPUs completely change this equation. They give you instant access to powerful computing including storage, networking, DevOps and more without the upfront cost or complexity of ownership. No matter if you’re training a massive language model or fine-tuning a small vision network, cloud GPU providers let you spin up GPUs in minutes and for a really low price. 

For example, instead of buying an NVIDIA H100 PCIe GPU for thousands of dollars, you can rent the same for just $1.90/hour. This means you’re getting a high-performance cloud GPU for AI when you need it and for as long as you need it. No maintenance or depreciation, just pure performance on demand.

With cloud GPUs, you can:

• Train, test and deploy AI models at scale without building physical infrastructure.
• Start small and scale by choice, only paying for what you use.
• Experiment freely, trying different model architectures or batch sizes without worrying about sunk costs.
• Collaborate globally, since your environment can be accessed from anywhere.

Why Cloud GPUs are Ideal for Startups and Individuals

For startups and individuals, every penny and every hour count. Cloud GPUs remove the financial and operational barriers that come with setting up infrastructure from scratch.

• No upfront costs: Instead of spending capital on hardware, startups can direct funds toward model development, talent and growth.
• Pay-as-you-go pricing: With hourly billing, you pay only for what you use, whether that’s a few hours of training or a full week of testing.
• Instant scalability: Need to scale from one GPU to ten overnight? No problem. Cloud GPUs scale effortlessly to meet demand.
• Freedom to experiment: Try different frameworks, architectures or models without worrying about capacity.

Why Cloud GPUs Make Sense for Enterprises

Enterprises face a different challenge which includes managing massive and distributed workloads while keeping costs predictable and ensuring security and compliance. Cloud GPUs let you solve this by providing high performance at scale, without the need to manage infrastructure manually.

With cloud GPUs, enterprises can:

• Accelerate time to market by provisioning compute instantly instead of waiting months for procurement and setup.
• Run hybrid workloads, mixing on-prem compute with cloud-based clusters for peak efficiency.
• Scale global teams by giving every developer or data scientist secure access to high-performance infrastructure.
• Control costs through usage-based or reserved pricing.

Why Run Your AI Workloads with Hyperstack Cloud GPU VMs

Hyperstack offers a real high-performance cloud environment that let users build market-ready AI products faster. From training and fine-tuning to inference and large-scale deployment, our cloud platform gives you the performance and flexibility you need without any hidden costs.

1. A GPU for Every Workload

Whether you’re training massive transformer models or running smaller inference tasks, Hyperstack offers instant access to a wide range of NVIDIA GPUs including NVIDIA H100 PCIe/SXM, NVIDIA A100, NVIDIA L40 and NVIDIA RTX A6000 along with CPU-only options for lightweight tasks.

Need NVLink for high-bandwidth interconnects? Hyperstack’s got that covered too. You can choose NVLink configurations (H100 GPUs) optimised for your workload for maximum performance and efficiency.

2. No Oversubscription, Ever

Unlike shared cloud platforms that overcommit GPU resources, Hyperstack guarantees zero oversubscription. This means you always get the full performance of your GPU, even under heavy workloads. No slowdowns. Just consistent and peak performance every time.

3. High-Speed Networking and Optimised Storage

AI performance is just about GPU compute. It is also about how fast your data moves. Hyperstack supports networking speed for NVIDIA H100 and NVIDIA A100 GPUs with NVMe root disks and ephemeral NVMe storage. This ensures rapid data access and minimal latency across all workloads.

4. Pause, Save and Resume Anytime

Training an AI model but need to pause overnight? With Hyperstack’s hibernation feature, you can suspend workloads, save progress and resume later without paying for idle resources. It’s a simple way to control costs while maintaining workflow continuity.

5. AI-Optimised Kubernetes

For teams running complex containerised workloads, Hyperstack provides on-demand, fully optimised Kubernetes clusters. These come with NVIDIA drivers, high-speed networking, and scalable storage preconfigured, so you can easily orchestrate large AI/ML jobs.

6. Performance-Tuned for Parallel Workloads

Our NUMA-aware scheduling and CPU pinning help align compute, memory and GPU within the same zone. This reduces latency and boosts performance for compute-intensive tasks, such as deep learning, simulations, or generative AI training.

7. Developer and DevOps Friendly

Whether you’re provisioning with Terraform, automating workflows via Python or Go SDKs, or deploying using built-in APIs, Hyperstack simplifies infrastructure management. We also provide an LLM Inference Toolkit for running large models across cloud or on-prem environments seamlessly.

8. Transparent Pricing and Hassle-Free Deployment

Getting started on Hyperstack takes minutes with 1-click deployment and a clean, transparent pricing model. No egress fees. No hidden costs. You get enterprise-grade performance with complete cost control and direct, engineer-led support when you need it.

Our Cloud GPU Pricing 

Hyperstack offers both on-demand and reserved pricing, giving you flexibility to choose based on your project’s duration and budget.

• NVIDIA RTX A6000 for $0.50/hour
• NVIDIA A100 for $1.35/hour
• NVIDIA H100 SXM for $2.40/hour
• NVIDIA H100 PCIe for $1.90/hour

For long-term projects, reserved pricing offers even greater value. For example, reserving an NVIDIA H100 SXM can start as low as $2.04/hour, making it one of the most cost-efficient ways to scale demanding workloads over time.

Conclusion

Running AI workloads no longer requires racks of GPUs humming in a data centre. With cloud GPU providers like Hyperstack, you can access top-tier compute power instantly, scale seamlessly and pay only for what you use. It’s faster, more flexible and far more cost-effective than building and maintaining your own infrastructure.

Whether you’re a small startup training your first model or an enterprise deploying large-scale AI pipelines, cloud GPUs let you focus on what truly matters: innovation. And with Hyperstack, you get more than just access to hardware. You get a performance-optimised, developer-focused ecosystem built to power the next generation of AI.

Run your next AI workload the smarter way!

Spin up a GPU VM on Hyperstack today and experience performance without limits.

FAQs

What are Cloud GPUs and how do they work?

Cloud GPUs are high-performance graphics processors hosted by cloud providers. They let users run AI, ML and data workloads remotely, offering scalable compute power without owning physical hardware.

Why should I use Cloud GPUs for AI workloads?

Cloud GPUs provide on-demand, scalable compute power for AI workloads. They reduce costs, eliminate hardware maintenance and enable faster training, fine-tuning and deployment of machine learning and generative AI models.

Are Cloud GPUs cost-effective compared to on-premise hardware?

Yes, Cloud GPUs are far more cost-effective. You pay hourly or per-second rates instead of investing thousands upfront, making them ideal for startups, researchers and enterprises scaling AI workloads efficiently.

How does Hyperstack differ from other Cloud GPU providers?

Hyperstack offers zero oversubscription, AI-optimised Kubernetes, high-speed networking up to 350 Gbps and transparent pricing. It’s purpose-built for AI workloads, ensuring peak performance and cost efficiency for every project.

Can I train large AI models like Llama 3 on Cloud GPUs?

Absolutely. Platforms like Hyperstack let you train, fine-tune and deploy large AI models such as Llama 3 using NVIDIA H100 or NVIDIA A100 GPUs for maximum performance and scalability.

What pricing options are available for Cloud GPUs on Hyperstack?

Hyperstack offers flexible on-demand and reserved pricing. GPU options include NVIDIA RTX A6000, NVIDIA A100 and NVIDIA H100, starting from as low as $0.50/hour for cost-effective AI compute.

Are Cloud GPUs secure for enterprise AI workloads?

Yes. Cloud GPUs like those on Hyperstack feature enterprise-grade encryption, role-based access control, and isolated environments, ensuring data security and compliance for enterprise AI and ML workflows.