Looking for a simple way to run AI workloads without buying expensive hardware? GPU-as-a-Service (GPUaaS) gives you instant access to high-performance GPUs without the upfront investment of traditional infrastructure. Instead of paying for physical servers, you only pay for the compute you use.
Unlike conventional setups that demand ongoing maintenance, upgrades and high electricity costs, GPUaaS turns GPU power into a flexible operational expense. And with platforms like Hyperstack offering NVMe storage, networking and 1-click deployment, you get an edge in both performance and ease of use.
Continue reading as we break down what GPUaaS really means, the benefits it brings and why Hyperstack stands out as the provider built for modern AI teams.
Understanding GPU-as-a-Service and How It Works
GPU-as-a-Service (GPUaaS) allows users to deploy GPU resources in the cloud rather than owning and maintaining them on-site. By using a cloud GPU rental platform, companies can run compute-heavy tasks like AI training, machine learning, deep learning, data analytics, and HPC applications without any upfront GPU investment.
GPUaaS transforms GPU infrastructure from capital to operational expense, providing on-demand GPU services with scalable, usage-based billing. This makes it easier for teams to innovate faster, without the burden of managing physical GPU infrastructure.
On-Premises GPUs vs. GPUaaS
Here’s a concise comparison to help you see which model fits your needs:
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Feature
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GPUaaS (Cloud)
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On-Premises GPU
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Upfront Cost
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None; pay-as-you-go
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High CapEx for hardware
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Scalability
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Elastic; scale up/down instantly
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Fixed capacity; difficult to scale
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Access to Latest GPUs
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Immediate access
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Requires costly hardware refreshes
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Maintenance & Support
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Managed by the provider
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Requires an internal IT team
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Ideal for
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Variable workloads & experimentation
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Predictable, steady state workloads
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Time to Deploy
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Minutes
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Weeks to procure and install
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On-premises GPU infrastructure offers full control, predictable performance, and lower latency, suitable for organisations with steady, long-term GPU needs. But this comes at a high cost due to hardware, maintenance, upgrades, and energy usage.
On the other hand, GPU platform as a service like Hyperstack eliminates capital expenses by offering on-demand access to powerful GPU platforms. With flexible pricing and GPU cloud service models, organisations can easily scale workloads up or down as needed.
Why GPU Cloud Services Are Rapidly Growing
In 2023, the global GPU-as-a-service market was valued at $3.23 billion and is expected to increase from $4.31 billion in 2024 to $49.84 billion by 2032 [source]. This massive growth for cloud GPU services is driven by the following factors:
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Growing Demand for AI, Machine Learning and LLM
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Rising popularity of cloud gaming
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Growth in Data Analytics and Real-Time Processing Workloads
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Infrastructure and Operational Cost Savings
Benefits of GPUaaS
Here’s why more businesses are switching to GPUaaS providers like Hyperstack for their compute-intensive workloads:
- GPUaaS allows platforms like Hyperstack to spin up anywhere from a single GPU to thousands on demand, ensuring you only provision what you need. This is perfect for workloads that fluctuate or for experimentation.
- Instead of paying $25,000+ per GPU card upfront, you pay per hour (e.g., $1.90–$2.40/hr for the same GPU to rent it). This can dramatically reduce the total cost of ownership (TCO), especially for projects with unpredictable usage patterns.
- Cloud platforms handle OS updates, hardware failure management, cooling, networking, and performance monitoring, letting you focus on core workloads, not infrastructure.
- Teams worldwide can access GPUs through the cloud, enabling distributed development and shared resources without physical hardware shipping or setup.
- With options like on-demand billing, reserved instances or hibernation/spot discounts, GPUaaS gives you multiple levers to reduce cost.
Challenges of GPUaaS
Despite the advantages, there are challenges associated with using GPU as a service:
- Latency: As a cloud-based GPU platform, GPUaaS may introduce delays depending on internet quality.
- Compatibility: Migrating legacy systems to a new GPU solution can require technical adjustments.
- Data Transfer Costs: While Hyperstack avoids ingress/egress fees, other GPU cloud services may charge more for data-heavy tasks.
GPUaaS Pricing: How It Works and What to Expect
One of the most attractive aspects of GPU‑as‑a‑Service is its flexible and usage‑based pricing model. Unlike traditional on‑premises GPUs that require large upfront investments and ongoing maintenance costs, GPUaaS allows teams to pay only for what they use, making it ideal for projects of any scale, from experimentation to enterprise-grade AI workloads.
Pricing Models
Pay-as-You-Go
Billed per hour based on the GPU type. NVIDIA H100 SXM on Hyperstack at $2.40/hour. Perfect for short-term or burst workloads.
Reserved GPU VMs
Commit to longer usage (e.g., 1–12 months) to access lower rates. Reserved NVIDIA H100 PCIe GPU on Hyperstack at $1.90/hour. Ideal for projects with predictable compute needs.
Spot GPU VMs
Use idle GPUs at discounted rates, often 20% cheaper, with the trade-off of potential interruptions. Best suited for fault-tolerant workloads like distributed model training or batch processing.
Why Hyperstack Is the Ideal GPUaaS Provider for AI, HPC and LLM Workloads
Some of the popular cloud GPU providers could be Hyperstack, NexGen Cloud’s GPUaaS platform, lets you deploy workloads on NVIDIA GPU infrastructure, paying only for usage. Here's why it's ideal:
- Access to Cutting-Edge GPUs: As an NVIDIA Preferred NCP Partner, we are Cloud GPU providers for AI and other demanding workloads.
- Flexible Pricing: With minute-by-minute billing, pay only for what you use. No hidden fees, and options for reservations and hibernation reduce costs.
- Easy Deployment: Our platform offers 1-click deployment, simplifying setup so you can focus on projects.
- Advanced Storage and Networking: We provide high-performance storage and Ethernet with up to 16 Gbps inter-VM bandwidth, reducing latency and boosting throughput.
- Seamless Scaling with NVLink: NVLink supports 600 GB/s bandwidth for high-end NVIDIA GPUs, crucial for large ML and AI projects.
- Support for Open-Source Models: Hyperstack supports open-source AI models, allowing integration of various tools and frameworks. Deploy models like Meta's Llama 3 easily.
- AI Studio: Test, train and deploy market-ready Gen AI products with our full stack Gen AI platform.
- Security and Control: We prioritise security with role-based access control and advanced firewall rules for a secure infrastructure.
Explore the Hyperstack GPU Cloud Platform
Ready to try one of the most scalable GPU cloud services available? Take a quick tour of Hyperstack and explore our high-performance GPU platform for your AI, ML, and data workloads.
Conclusion
As AI adoption grows, organisations need GPU solutions that scale. GPU-as-a-Service offers flexible, secure, and powerful infrastructure for enterprise workloads—without upfront investment. With Hyperstack’s sustainable GPU cloud platform, you get performance, flexibility, and eco-conscious GPU services. Whether you’re a startup or enterprise, our platform is built for your next big idea.
Sign Up Now to Get started with Hyperstack.
FAQs
What is GPU-as-a-Service?
GPUaaS lets users access GPU infrastructure in the cloud, removing the need for in-house hardware and offering scalable GPU services.
What are the main benefits of using GPUaaS?
The key benefits of using GPUaas include scalability, flexible payment models, enhanced data security, and faster time-to-market for AI and ML applications.
Can I scale my GPU resources with Hyperstack?
Yes, Hyperstack allows seamless scaling of GPU resources to meet changing workload demands, ensuring optimal performance.
How is pricing handled in GPU as a Service?
Hyperstack is a GPUaaS provider with usage-based billing. You only pay for what you use—no hidden fees.
Can I use GPUaaS for LLM training?
Yes, Hyperstack supports LLM training and inference via its high-performance GPU cloud platform optimised for AI.
What infrastructure or platform features should you look for when choosing a GPUaaS provider?
Look for high-performance GPUs, fast NVMe storage, networking, scalability, flexible billing, data security and easy deployment tools—ensuring your workloads run efficiently and cost-effectively without hardware management overhead.
What are the main benefits of using GPUaaS?
GPUaaS offers on-demand access to powerful GPUs for AI, ML, and HPC workloads—eliminating upfront costs, enabling rapid scaling, simplifying infrastructure management, and accelerating training, inference or rendering pipelines across projects.
How do you get started with GPUaaS?
Sign up on your chosen platform, select a GPU type, configure your instance, and deploy via console or API. Many providers offer quick-start guides and 1-click deployment options for beginners.
How do I get started with Hyperstack?
Sign up for a Hyperstack account by visiting https://console.hyperstack.cloud/. After registration, sign in, activate your account and add credit to deploy your virtual machine on Hyperstack. Learn more about getting started in our documentation here.
