Demystifying GPU-as-a-Service (GPUaaS) for your next AI project

Graphics Processing Units (GPUs) first came onto the scene in the early 1990s. Their main job was to make video games look better by improving how 3D animated objects were shown on computers. Think of the shift from pixelated and blurry interfaces in the early Pokémon video games to realistic 3D-FPS games like Quake III Arena. This was thanks to the revolutionary introduction of Nvidia GeForce 256 that took the gaming community by storm and changed the way we experienced virtual worlds forever.

What makes GPUs special is their ability to perform many tasks at once because of their parallel architecture design. This means they can break down big computational problems into smaller parts and solve them all simultaneously. In the world of computer graphics, GPUs represented a quantum leap in rendering high-quality images and videos that involve complex processing of graphics data for shading, lighting, and texturing. Before GPUs were around, CPUs had to handle these heavy tasks one at a time.  

With GPUs, the demanding real-time calculations needed for transforming 3D objects and simulating lighting effects in videos or computer-aided design (CAD) applications can be shifted from the CPU to the GPU. This change meant faster and more efficient rendering in computers, leading to smoother gameplay and realistic visuals — in-game characters now had smooth edges, lifelike textures, and colored pixels were no longer visible.

A red toy truckDescription automatically generated
Figure 1: GPUs enable realistic virtual representations of real-world objects by processing spilt-second computations of various graphical variables (Source: Nvidia)

As game developers rapidly embraced this new technology, it became clear that GPUs are incredibly good at handling multiple computations quickly and efficiently — far better than CPUs. The fact that they also come packed with features like CUDA cores, shader units and have a sophisticated memory hierarchy made them fundamental in today's 2nd AI wave. Take modern GPUs like Nvidia's A100 or H200; they offer top-notch performance not just for big data analytics but for deep learning training and various data centre applications using accelerated computing — an Nvidia concept which refers to using specialized hardware for faster parallel processing.

Despite their power and importance in real-time data processing and AI training, getting access to these chips can be challenging due to supply shortages and high costs associated with running energy-intensive data centers filled with specialized equipment. Larger corporations have proven to possess much better chances at getting access to specialised GPUs due to their size, deep pockets, market positions and surprisingly, industry connections. And this has left smaller start-ups, researchers and less-‘techy’ companies scrambling for these silicon cards.

Furthermore, enterprises are hesitant to make huge upfront commitments to procure expensive, specialized GPUs for their AI aspirations, as it can be tough for IT teams to provision for the increasing need for GPUs across projects. Typically, IT teams would provision and deploy a GPU server dedicated to a single application, thereby creating an internal bottlenecks as different teams wait their turn. Once provisioned, IT teams often aim to optimize the utilization of these powerful resources to ensure they achieve the best possible return on their GPU investment.

GPUaaS: The shovels of the AI goldrush

To address growing industry challenges in accessing GPU power in a rapidly evolving AI landscape, cloud giants (AWS, Google Cloud), IT providers (HPE, F5) and even telcos (Singtel, Indosat) are offering GPU-as-a-Service (GPUaaS). This service offers individual and enterprise users with on-demand, scalable and cost-effective access to GPUs via traditional cloud environments. Enterprises can leverage virtual GPUs, which are often included in fully managed platforms equipped with pre-installed tools and built-in GPU enablement. This setup allows them to run various AI applications efficiently while sharing resources flexibly across different teams within containerized environments. All this comes without the burden of managing physical infrastructure or making large upfront investments.

GPUaaS also allows enterprises to quickly experiment and launch AI pilots as computing infrastructure can be rapidly set up with pre-integrated frameworks and adjusted dynamically based on internal demand. For organizations that choose to focus on AI development within a handful of functions, GPUaaS platforms can speed up AI training and deployment with distributed processing by provisioning multiple unused GPUs for the same task. Additionally, these platforms often simplify lifecycle and resource management through zero-touch unified orchestration using a single-pane-of-glass style management console. This cloud-like experience offers enterprises flexible pay-as-you-go pricing, easy scaling options, and reliable uptime across global IT systems, ensuring sustainable MLOps.

Enterprise playbook to GPUaaS

Enterprises should evaluate their current requirements for GPUs by asking themselves these questions:

  • Is our current in-house infrastructure able to meet our computational demands, or are there limitations that could hinder our operations?
  • Do we have tasks that require heavy computational power, such as deep learning, 3D rendering, or complex simulations?
  • Do we have tasks that require heavy computational power, such as deep learning, 3D rendering, or complex simulations?
  • Do we require iterative training and fine-tuning of models?
  • Are we looking to quickly move from development to production, and could this be accelerated by GPU as a service?
  • Do our applications require real-time data processing and decision-making and distributed interference?
  • Are we in an industry that demands high precision, such as healthcare for medical imaging analysis?
  • Do our AI models require a large number of GPUs for training and inference during production?  

When choosing a GPUaaS provider, enterprises should consider adopting a multi-vendor strategy. This approach allows them the flexibility to experiment and take advantage of different capabilities offered by various vendors. While the computational performance of underlying GPUs tends to be consistent across providers due to most solutions using infrastructure from Nvidia or AMD, other factors are crucial for decision-making:

  1. Pricing Models: Understanding how costs are structured is essential, availability of free trials periods, bulk pricing.  
  1. Compatibility: Ensure there is alignment with existing tools, libraries, and frameworks.
  1. Data Protection Policies: Check for robust security measures including data storage locations and encryption methods.

Additionally, enterprises should evaluate the extra support and resources that GPUaaS vendors provide. For instance, Nvidia NGC offers a collection of ready-to-use containers, pretrained models and SDKs to speed up enterprise application deployment while Microsoft Azure enhances application performance with a broad range of Azure services such as Azure Synapse.  

As AI becomes increasingly critical for businesses to gain a competitive edge and deliver enhanced value, enterprises must view solutions like GPUaaS as pragmatic alternative to accelerate AI initiatives, especially during periods of hardware scarcity. The advancements in Gen AI and LLM technology will eventually be complemented by the democratization of computational power, making high-end GPUs accessible to organizations of all sizes and locations. Soon enough, the playing field will level, and compute power will no longer differentiate success and failure in the AI race. Winners will be those who succeed in developing solutions that can deliver tangible business value and drive innovation across industries.