What is GPU-as-a-Service (GPUaaS) or GPU Cloud?

As organizations continue to progress in developing and implementing artificial intelligence (AI) solutions, running, managing and supporting these solutions is complex and requires an updated approach. AI infrastructure is not the same as traditional IT infrastructure, and considerations such as how to support GPUs (graphics processing units) and having access to data centers with updated capabilities are essential to successfully supporting AI workloads.

A common question facing IT organizations is "Do I build, or do I buy?" Several options are available to run AI solutions, including on-premises high-performance architecture (HPA), privately hosted AI, Artificial Intelligence as a Service (AIaaS), and GPU-as-a-Service (GPUaaS) or GPU Cloud.

Each has specific benefits but given the rise of GPU-as-a-Service (GPUaaS) offerings and their relative newness, it is important to understand GPUaaS, its benefits, the best use cases and considerations in choosing a provider.

What is GPU-as-a-Service or GPU Cloud?

The demand for high-performance hardware has led to GPU-as-a-Service (GPUaaS) or GPU Cloud, offering on-demand GPU access in the cloud. GPUaaS provides high-performance computing for machine learning (ML), deep learning, and data-intensive tasks.

In simple terms, GPUaaS is renting or leasing GPUs from a service provider. Utilizing GPUs, GPUaaS delivers computational power without the need for expensive hardware or complex infrastructure management and often includes supporting managed services. Using GPUaaS also buys time for enterprises looking to move quickly with AI projects, as GPUaaS service providers are better at standing up and operating these environments.

Factors influencing adoption of GPU-as-a-Service

AI infrastructure has many new requirements, such as power, cooling and physical facilities, that most current data centers lack. As a result, several factors influence the decision on where to run AI solutions.

Costs: Accurately budgeting and managing AI projects is difficult as cost standards have yet to be defined. As a result, analysts predict that by 2028, over 50 percent of GenAI projects will exceed budget due to poor architecture and lack of operational expertise.* The high cost of AI compute resources, including specialized hardware and software, can be a barrier to entry for many organizations. In addition, containing the costs of infrastructure and determining a reliable TCO model is imperative.
Data center facilities and scalability: As more clients launch AI initiatives, demand for data centers will increase and new data centers will emerge with updated facilities that include refined designs and power and cooling capabilities built to fully support AI workloads. In addition, the AI footprint for a large enterprise is smaller than traditional IT, so retrofitting an existing data center for AI may not be cost-effective if the AI workload is minimal. To accommodate these demanding AI workloads, businesses will require compute solutions that can seamlessly scale to handle increasing data volumes and model complexity.

Recommended reading: WWT Research: Facilities Infrastructure Priorities in the Age of AI
Optionality and vendor lock-in: Organizations need choices among NVIDIA, AMD, Intel Accelerators, etc. This includes GPUs, TPUs (tensor processing units) and CPUs (central processing units). In addition, vendor lock-in is a major concern for businesses adopting AI, as it can limit flexibility and increase costs in the long run.
Power: AI technology requires a lot of power to support it and finding that power with all the new demand is challenging. The power demand in data centers, which has remained stable for the past decade, is projected to double by 2030. Consequently, clients will need to develop a comprehensive energy strategy to address this increase. In addition, AI infrastructure requires higher levels of power to run, and retrofitting a data center can take months or even years. Enterprises will need to make decisions on when and where to deploy AI projects in both the near and long term.
Operations: Deploying and managing AI infrastructure can be complex, requiring specialized expertise and tools. HPC environments (GPUaaS is an example) are different from IT environments, and the operational model is different. In the next four to five years, more than half of enterprises creating their own models will abandon them due to high costs, complexity and technical debt. Clients need operationally consistent capabilities to succeed.

GPUaaS advantages (versus building your own)

So why use GPUaaS? Many organizations can't afford to build a new data center and buy expensive hardware to run their AI solutions, so GPUaaS provides a high-quality option with many advantages.

Cost effectiveness: Eliminates the need for upfront investment in accelerated compute and supporting infrastructure/facilities.
Time to market: Quickly deploy infrastructure for new AI/ML services and expand them into new geographies
Hardware accessibility: Easy access to accelerated infrastructure that an on-premises facility might not be ready to host.
Scalability and elasticity: Self-service and on-demand infrastructure whenever resource-intensive AI/ML workloads need it.
Advanced facilities: Secure, world-class data center facilities equipped to efficiently handle the requirements of AI/ML.
Simplified management: Focus on core business activities rather than managing supporting infrastructure and facilities.
High availability and global reach: Deploy to data centers located around the world to reduce latency and improve AI/ML experiences.
Workload portability: Organizations can bring their desired AI/ML ecosystem with them, including tools, frameworks, models, and the existing codebase.
Data security and compliance: Provides robust security measures to protect customer information while adhering to stringent compliance standards.
Faster proof of value (POV): Many organizations are still in the POC (proof of concept) and POV phases of their AI deployments. A GPUaaS environment is a low-risk way to address these risks by working with these workloads at scale.

GPUaaS use cases

GPUaaS solutions can be used for a variety of AI use cases and serve a variety of business needs. The most common applications are the following:

Machine learning and deep learning: GPUs offer substantial acceleration in the training of intricate models on extensive datasets, allowing data scientists to iterate rapidly and enhance model precision.
Data processing and analytics: Numerous large-scale data processing tasks, including sorting or filtering, benefit from the parallel computing capabilities provided by GPUs, enabling organizations to manage vast quantities of data with greater efficiency.
High-performance computing (HPC): Scientific simulations, financial modeling and other computationally intensive workloads can leverage GPU acceleration to significantly reduce time to solution.
Gaming and virtual reality: Cloud-based gaming services rely heavily on advanced GPUs for high-quality, real-time graphics rendering, ensuring an immersive user experience.

Selecting GPU-as-a-Service providers

Several established GPUaaS providers, including Scott Data Center, Coreweave and Lambda Labs, are successfully delivering high-quality solutions.

Here are a few things to consider when selecting a GPUaaS provider:

Assessing performance: The first factor to consider is the performance of available GPUs. Providers offer different levels of processing power based on their hardware resources. It is important to compare GPU specifications, such as memory capacity and compute capabilities, and benchmark GPUs to determine if a provider's offering meets project requirements and provides sufficient performance for actual workloads.
Analyzing cost efficiency: Budget limitations frequently influence the selection of a GPUaaS platform. Providers typically charge based on usage duration or allocated resources such as storage space and bandwidth. Consequently, it is essential to thoroughly review pricing models.
Reviewing data security and compliance: Data security is a critical consideration when selecting a GPUaaS provider. Ensure the platform adheres to industry regulations and implements strong security measures to protect sensitive information. It is also important to review each provider's policies on data storage locations and encryption methods used during transmission.

Learn more about GPU-as-a-Service

As organizations advance in the development and implementation of AI solutions, the tasks of operating, managing and maintaining these solutions become increasingly complex, necessitating a modernized approach. GPUaaS provides a high-quality option for many businesses. To learn more about how your enterprise can benefit from GPUaaS offerings, please contact Chris Campbell or your WWT account team.

*Gartner "10 Best Practices for Optimizing Generative AI Costs" – June 6, 2024