GPU PaaS Unleashed: Empowering Platform Teams to Drive Innovation

GPUs underpin cutting-edge AI, machine learning, and big data workloads. They also provide critical acceleration for simulation, video rendering, and streaming tasks. With modern enterprises likely to be investing in some or all of these fields, easy access to GPU devices is essential to sustain innovation.

Platform teams often find it challenging to operate cloud GPU instances in multi-cloud environments. Building a GPU PaaS (Platform as a Service) removes the complexity by letting you manage, scale, and optimize GPU workloads across your infrastructure. The PaaS pools your GPU resources and provides crucial governance controls.

This article will explore the benefits of GPU PaaS and how such offerings help platform teams, data scientists, and app developers. We’ll discuss how you can leverage the Rafay Platform to deliver a GPU PaaS and innovate faster by streamlining infrastructure management, enabling self-service access, and accelerating AI model training workflows.

What is GPU PaaS?

GPU PaaS is a platform-driven approach to cloud GPU hardware consumption. It solves the challenges around multi-cloud GPU management at scale by combining GPU device pooling and virtualization with centralized governance features, along with the AI tooling needed by developers and data scientists to develop AI-based applications.

GPU PaaS builds upon  GPUaaS (GPU-as-a-Service) offerings. GPUaaS delivers access to raw GPUs through connectivity primitives (such as SSH) and is preferred by highly sophisticated teams that have enormous internal skillset when it comes to building their own PaaS offerings. GPU PaaS adds a platform layer that not only orchestrates GPU allocation and enables more control over the infrastructure, but also brings a number of AI tools that developers and data scientists need to build AI applications.

Here’s a summary of how the two techniques compare:

• GPUaaS: Teams get direct access to servers with GPUs, and manage the infrastructure on their own without any additional software support from the provider.
• GPU PaaS: In addition to being allocated GPUs from a (usually shared) pool of resources, the provider also offers a number of software infrastructure and tooling options to simplify AI application development for enterprises and ISVs.

GPU PaaS is an important investment for enterprises building complex big data or generative AI workloads, especially those using multi-cloud environments. Pooling GPUs from different cloud providers enables more flexible hardware procurement and utilization. Models in any cloud can scale up effortlessly by tapping the pool of available GPUs.

How GPU PaaS Supports AI and Cloud Operations

GPU PaaS optimizes AI and cloud operations by orchestrating GPU resources. Similarly to how container orchestrators like Kubernetes enable efficient use of compute capacity, GPU PaaS applies similar principles to GPU devices. Pooled GPUs, virtualization support, and partial GPU shares for workloads allow GPU compute capacity to be used much more effectively.

These features have a positive benefit on all kinds of GPU-based applications:

• AI model training is accelerated because GPU access is always available. The most performant hardware (such as NVIDIA H100 GPUs and NVIDIA T4 inference cards) can be reserved for use with AI infrastructure to minimize contention.
• Video rendering can be easily parallelized across clouds by pooling high performance GPUs from different providers. Mixing NVIDIA cards and AMD GPUs can also facilitate game development and testing workflows, where good compatibility with both vendors is required.
• Simulations and research work benefit from simpler access to GPU hardware for developers and data scientists. Self-service workflows enable efficient iteration without having to manually configure the underlying hardware for each run. This means more results in less time.

Leveraging these features depends on access to a GPU PaaS solution that seamlessly supports hybrid and multi-cloud configurations. Rafay enables these workflows by making it easy to standardize every part of your cloud operations, then view all infrastructure—including cloud, bare metal, and on-premises—in one place. You can create a GPU PaaS that spans each cloud provider you use, with zero risk of vendor lock-in.

Benefits of GPU PaaS for Platform Teams

The benefits of GPU PaaS extend to platform teams tasked with building and maintaining GPU cloud infrastructure. The GPU PaaS computing model simplifies common management tasks, enabling several operational improvements:

• Simple self-service provisioning: Developers can use the platform to access available GPU resources on-demand. The platform will provide the GPU instances that are most appropriate for the workload that’s been deployed. This gives platform teams certainty that GPUs aren’t being wasted.
• Automated workflow optimizations: GPU PaaS facilitates automatic infrastructure optimizations that improve operations over time. The platform can monitor GPU usage, then automatically redistribute workloads across the connected cloud providers. This helps keep demanding apps running smoothly while reducing the administrative burden on the platform team.
• Effortless scalability: Abstracting GPUs into a PaaS enhances scalability. If a workload needs to scale up, it can simply request more GPU resources from the pool. Resizes become quick and effortless, instead of requiring manual work to connect new GPUs and make them available to your apps.
• Continuous governance and compliance: Platform teams can achieve continuous governance of GPU resources using the controls provided by the PaaS. You can limit how much GPU capacity is available to specific teams and projects, for example, or define how workloads are spread across cloud services.

GPU PaaS takes the advantages of app-level PaaS solutions and applies them to GPU resources. This enables more consistent GPU management that meets the needs of everyone involved with GPU workloads. It facilitates high performance deployments of generative AI, neural networks, and machine learning apps, without the risks of non-compliance caused when basic GPUaaS solutions are used. Clear visibility into GPU allocation is essential: sensitive workloads such as AI training runs must only execute in environments that meet relevant legal and ethical requirements. GPU PaaS provides the necessary visibility and control.

Using Rafay for GPU PaaS Management

Rafay is the ideal platform for building your GPU PaaS. GPU-powered workloads are fully supported within Rafay’s cloud-native infrastructure management solution. You can launch your GPU PaaS within hours by bringing your existing GPU infrastructure into Rafay.

Rafay’s solution fulfills all the key requirements of an effective GPU PaaS. You can create pools of GPU hardware resources that span multiple data centers and cloud providers, then enable developer access by creating self-service workflows that are presented in a “storefront”-like experience. Platform teams can monitor GPU usage, set GPU/environment matchmaking rules, and centrally enforce governance policies that implement security and compliance requirements.

Rafay’s key features for GPU PaaS include:

• Self-service GPU Consumption: Empower developers & data scientists to consume GPU resources on demand
• AI Apps delivered as a Service: Templatize and package AI/ML apps on the Rafay Platform for as-a-Service delivery
• Multi-tenant Clusters: Maximize your investment by supporting multiple customers on shared infrastructure

Rafay also lets you configure self-service GPU-enabled workspaces that AI engineers and data scientists can use as development environments. Instead of painstakingly configuring local workstations with high-end NVIDIA GPUs and complex software stacks, Rafay allows you to effortlessly start Jupyter Notebooks, use VS Code integration, and run other critical tools in the cloud.

To summarize, building a GPU PaaS with Rafay lets you manage your GPU infrastructure as a single resource. You can precisely orchestrate GPUs and their workloads to prevent waste and maximize operational efficiency. Rafay provides clear visibility into which workloads are using GPUs and where they’re running.

• GPU PaaS for Fine-Tuning and Serving AI Models: Rafay streamlines fine-tuning and serving (inferencing) AI models by providing seamless deployment across multiple clouds. Customers can efficiently leverage their cloud providers’ most performant GPU hardware classes, ensuring optimized model performance and scalability.
• GPU PaaS for Video Rendering: GPU PaaS enhances cross-organizational video rendering workflows, allowing teams to utilize GPU hardware more efficiently. Rafay facilitates workload distribution between clouds, improving processing throughput and enabling data to remain within the originating cloud for better security and speed.
• GPU PaaS for Simulation and Research Teams: Rafay simplifies self-service workflows for teams working on large-scale simulations and advanced research projects. By granting developers on-demand access to GPUs, Rafay eliminates bottlenecks typically caused by waiting for infrastructure provisioning. This accelerates iteration cycles and boosts team efficiency.

These cases represent a few key success stories, but GPU PaaS implementations aren’t restricted to these fields. Creating your own GPU PaaS with Rafay empowers you to optimize your GPU-based workloads for greater efficiency, resiliency, and cost-effectiveness. If you’re running GPUs in the cloud, then you’ll benefit from creating a GPU PaaS.

Conclusion

Running AI, deep learning, and video workloads at scale depends on efficient multi-cloud GPU access. Building a GPU PaaS enables researchers, data scientists, and developers to access GPU instances as they need them. Simple self-service workflows accelerate deployment times, while automated central governance options keep platform teams in control.

The Rafay Platform is the shortest and most cost effective path for enterprises and service providers to launch GPU PaaS offerings. Rafay lets you easily manage GPU device pools, provide “storefront” access for developers, and configure virtual GPU allocation in multi-cloud environments. Built-in monitoring, multi-tenancy, and governance capabilities enable platform teams to confidently manage GPU access while development teams innovate faster.

Schedule a demo to see Rafay in action and begin building your GPU PaaS.