GPU PaaS™ (Platform-as-a-Service) for AI Inference at the Edge: Revolutionizing Multi-Cluster Environments

Enterprises are turning to AI/ML to solve new problems and simplify their operations, but running AI in the datacenter often compromises performance. Edge inference moves workloads closer to users, enabling low-latency experiences with fewer overheads, but it’s traditionally cumbersome to manage GPUs (Graphics Processing Units) in distributed infrastructure.

In this article, we’ll discuss how you can use Rafay and a GPU PaaS (Platform-as-a-Service) strategy to streamline GPU access for edge and multi-cluster environments. This empowers platform teams, developers, and data scientists to optimize GPU operations at scale, whether you’re working with AI/ML, data analytics, or other HPC workloads.

GPU PaaS and the Future of Edge AI

GPU PaaS applies Platform-as-a-Service principles to GPU access and management. It solves the challenges of efficiently operating GPUs at scale by letting you pool GPUs across clouds, split them into virtual units, and configure centralized governance policies.

Implementing a GPU PaaS enables you to bring AI inference to the edge in a more cost-effective way. You can deploy your high performance computing workloads to locations close to users, then connect GPUs via a pooled resource. GPUs can be sourced from different cloud providers, enabling you to mix hardware tiers and avoid costly local rentals.

GPU PaaS transforms AI edge inference by offering the following benefits:

• Stable low-latency performance: GPU PaaS makes it easier to bring GPU-accelerated workloads closer to your users, ensuring low-latency operations.
• Enhanced resource flexibility in distributed environments: GPU PaaS allows you to optimize GPU allocation by dynamically partitioning physical instances into virtual units. This improves operational flexibility.
• Improved cost effectiveness at scale: GPU PaaS reduces AI inference operating costs by letting you source cheaper GPU instances that are located closer to users.
• Adapts to bare-metal deployments and edge environments: GPU PaaS pools GPU instances together, enabling you to manage both bare-metal and edge deployments with one consistent strategy. This reduces the operational overheads of complex AI/ML environments.

GPU PaaS builds upon the capabilities of workload orchestrators like Kubernetes. Kubernetes connects compute resources across edge and datacenter environments, letting you effortlessly scale app instances throughout that infrastructure. GPU PaaS facilitates a similar approach to GPU access, enabling your Kubernetes workloads to consume GPUs pooled from multiple different providers.

Using Rafay for Your GPU PaaS: Key Features and Differentiators

The Rafay platform is the premier option for building and operating a GPU PaaS. It augments Kubernetes with powerful GPU management capabilities so you can accelerate AI/ML adoption.

Rafay provides everything you need to implement a robust GPU PaaS. It consolidates Kubernetes clusters and GPUs across cloud and edge environments, facilitates self-service GPU access for developers and data scientists, and includes enterprise-grade governance controls.

Let’s look at Rafay’s key Kubernetes and GPU management features in more depth.

Kubernetes Lifecycle Management for Simplified Workload Orchestration

Rafay automates lifecycle management for your Kubernetes clusters, regardless of the environments they’re running in. You can control hundreds of clusters using one consistent interface, providing clear visibility into what’s running.

Rafay also makes it simple to connect GPUs to your clusters. Developers and platform teams can effortlessly operate Kubernetes apps that require GPU cloud computing capabilities. Workloads running in your clusters can consume the GPU resources you make available in Rafay, accelerating time to market for your AI/ML projects.

Dynamic GPU Resource Allocation for Cost-Efficient Scalability

Rafay includes powerful GPU resource allocation capabilities that help prevent resource wastage. GPU virtualization allows hardware to be dynamically partitioned into smaller units, meaning more deployments can be served from a single physical GPU. This feature can be paired with automated matchmaking policies that ensure each workload receives a share of GPU capacity that’s appropriate to its needs.

Using Rafay to pool all your cloud GPUs into a single resource translates to higher GPU utilization and improved ROI. Your hardware won’t sit idle waiting for statically allocated workloads to use it. Instead, you can responsively scale GPU assignments based on actual demand and precise prioritization rules. This ensures expensive performance hardware like NVIDIA H100 GPUs serve the AI/ML workloads that most need them, but can still be accessed by other deployments when spare capacity is available.

Enhanced Multi-Cluster Management for Seamless Operations

Adopting a multi-cluster infrastructure strategy can improve performance and redundancy at scale, but is traditionally challenging for platform teams to manage. You need clear visibility into where your clusters are located and what’s running in them, as well as the ability to deploy consistent governance policies across the whole fleet. GPU access requirements add further complexity and cost, typically requiring a new GPU fleet to be provisioned for each cluster.

Rafay natively supports multi-cluster Kubernetes management and GPU access. It provides a single centralized platform for administering all the clusters you use, whether they reside in the cloud, on bare-metal datacenter hardware, or on-premises. Clear dashboards give you visibility into all aspects of cluster operations, including GPU allocation and utilization stats. This empowers you to move more workloads into dedicated edge clusters, facilitating performant AI inference wherever it’s needed.

Integration with Bare-Metal and Edge Infrastructure for High Performance Workloads

Rafay’s platform includes Rafay MKS, an enterprise Kubernetes distribution that simplifies cluster operations within datacenters and edge environments. You can use MKS to easily provision edge clusters with access to NVIDIA GPU instances, directly from the Rafay dashboard.

Rafay’s hybrid operations model allows you to mix and match the infrastructure that best fits your workloads. You can use public cloud clusters alongside MKS edge environments to balance performance, availability, and cost-effectiveness.

Real-World Applications and Use Cases for a GPU PaaS

The benefits of a GPU PaaS apply to a diverse range of use cases involving HPC workloads:

• Simplified edge deployments for latency-sensitive AI inference models: You can easily move AI inference to the edge by leveraging multi-cluster infrastructure that taps into pooled GPU resources.
• Optimized GPU utilization for multi-cluster environments: Partial GPU allocations let you utilize your resources more effectively, increasing performance while reducing costs.
• Cohesive visibility into GPU usage: Managing all GPUs in one consistent platform facilitates single-pane-of-glass visibility. Holistically GPU monitoring across workloads supports more informed infrastructure decision-making.
• Consistent enforcement of GPU governance policies: GPU PaaS enables dependable governance at scale by letting you centrally enforce allocation, utilization, and compliance policies. You can ensure that GPUs are used efficiently and protect sensitive AI inference and training workloads from the risks posed by non-compliant environments.

In summary, implementing a GPU PaaS with Rafay allows you to scale your AI/ML inference models with easy access to multi-cluster and edge environments.

AI at the Edge: Insights from Industry Leaders

It’s not just us talking about GPU PaaS, edge AI, and multi-cloud and cluster infrastructure. A December 2024 report from analysts S&S Insider projected the market for edge AI solutions will reach $13.7 billion by 2032, with a compound annual growth rate of over 29%. The report states that edge AI “is shaping the future of industry and technology,” with businesses increasingly seeking solutions that “implement AI algorithms at the data source, reducing latency and boosting productivity.”

Gartner expects to see “an explosion in edge AI use cases,” while the CNCF reports 56% of organizations operated multi-cloud infrastructure in 2023. Analysis by Sacra found GPU cloud services CoreWeave, Lambda Labs, and Together AI have been experiencing 1,000% year-over-year growth, demonstrating the demand for easy access to cloud GPU.

Nonetheless, conventional managed GPU-as-a-Service (GPUaaS) platforms can still be costly and difficult to connect to your infrastructure at scale. At Rafay, we believe GPU PaaS is the future. It solves the challenges of multi-cluster and edge GPU access by letting you pool and partition the GPUs you already control.

Best Practices for GPU PaaS Adoption

We’ve seen that building a GPU PaaS facilitates more effective AI inference infrastructure by blending multi-cluster, edge compute, and hybrid cloud workflows. However, to reap all the benefits it’s important you carefully design your environments for visibility, compliance, and control. Here are some key best practices to keep in mind.

1. Prioritize Edge Deployments

Operating as many workloads as possible at the edge improves performance by reducing latency for end users. Design systems as modular microservices with minimal dependencies to make them more portable to different environments. Clearly identify which services need GPU access, then use virtualization and dynamic partitioning to allocate an appropriate share of the available GPU hardware.

2. Plan for Governance and Security in Distributed Environments

Distributed environments are complex to govern and secure. You need clear visibility into what’s running in each cluster, along with its dependencies and relevant compliance requirements. GPUs must also be protected against unauthorized use to ensure they’re available for the high performance workloads that need them. Use a dedicated platform like Rafay to cohesively manage your distributed infrastructure.

3. Focus on Minimizing Operational Overheads

Overheads can rapidly build up into big inefficiencies at scale, especially for demanding AI/ML workloads involving large datasets. It’s good practice to regularly audit your infrastructure to identify potential misconfigurations and optimization opportunities. Pooling GPU resources, running workloads at the edge, and using dynamic GPU partitioning improves scalability and flexibility, while collaboration enhancers such as self-service GPU access enable you to maximize productivity.

Conclusion: Use a GPU PaaS to Achieve Multi-Cloud AI Inference at the Edge

GPU PaaS strategies transform how GPUs are consumed in edge and multi-cluster environments. Using Rafay to implement a GPU PaaS allows you to save on hardware procurement costs, gain visibility into GPU utilization, and enforce governance policies that prevent waste and maintain compliance.

Moreover, Rafay’s GPU management capabilities empower you to confidently move your existing GPU-accelerated workloads to the edge. This improves operational efficiency while reducing latency for AI/ML end users. Your infrastructure will be simpler, more scalable, and less expensive to operate.

Book a demo today to experience Rafay’s leading GPU workload optimization features and drive more AI/ML innovation in your teams.