Traditional GPU infrastructure requires significant investment in physical hardware leading to high upfront costs due to ongoing maintenance, upgrades and electricity. With GPUaaS, companies can eliminate the need for upfront hardware investments and convert GPU usage into an operational expense, paying only for what they use. Continue reading as we explore what GPU-as-a-Service means, its key benefits and why Hyperstack can be your preferred GPUaaS provider.
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 GPU cloud service, 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 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
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-as-a-Service 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 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 GPU cloud platforms like Hyperstack for their compute-intensive workloads:
- Scalability to easily scale GPU infrastructure for AI, ML, and analytics projects
- Flexible Payment for usage-based pricing from a trusted cloud GPU provider
- Data Security for secure and compliant GPU cloud services
- Faster Time-to-Market to deliver results quicker with ready-to-go GPU solutions
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.
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.
- 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.
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.
