What is a GPU PaaS?

GPU Platform as a Service (GPU PaaS) is a cloud-native model that gives developers and data scientists secure, on-demand access to GPU resources for running AI, GenAI, and ML workloads.Rafay’s GPU PaaS™ stack simplifies GPU delivery across any environment—enabling faster time-to-market and maximum return on GPU investments, allowing you to immediately monetize your GPU (which historically are quite expensive and difficult to access).

GPU PaaS is a specialized form of platform as a service designed to handle GPU-specific workloads, which are typically highly computational and resource intensive. It offers a fully operational GPU PaaS that integrates GPU hardware with cloud services, enabling customers to access, scale, and deploy GPUs with ease, transforming your existing GPU infrastructure into a high-performance GPU PaaS. Rafay’s comprehensive management tools ensure scalability, security, and operational efficiency for large-scale GPU clusters.

The brief history of GPU PaaS shows its emergence as a response to the growing demand for AI model development and the need for efficient GPU usage. Traditional PaaS solutions fall short in addressing the unique requirements of GPU-driven applications, prompting the rise of GPU PaaS as a critical component of AI infrastructure.

Traditional PaaS doesn’t solve GPU-specific challenges because these platforms are primarily designed for CPU-based workloads and lack the capabilities to support the complex requirements of GPU resources.  GPU PaaS simplifies GPU utilization at scale by providing organizations with powerful management tools, built-in governance, and infrastructure as code (IaC) automation. This enterprise-ready approach gives teams greater control, visibility, and cost tracking across their GPU resources.

Introduction to GPU Computing

GPU computing has revolutionized artificial intelligence, machine learning, and deep learning by providing high-performance processing capabilities. Developers and data scientists can now consume GPU resources on demand, accelerating workflows and reducing time-to-market.

With the advent of GPU PaaS (Platform as a Service), enterprises can transform their existing GPU infrastructure into a fully operational GPU PaaS in hours. This transformation empowers data scientists to rapidly access and deploy GPU resources without relying heavily on IT infrastructure teams or manual provisioning. By automating setup and configuration through infrastructure as code (IaC), a GPU PaaS reduces deployment time from days to minutes, enabling faster experimentation and model training.

By leveraging a GPU PaaS, organizations ensure their GPU resources are fully utilized, providing a scalable, reliable foundation for AI and ML initiatives.

How GPU PaaS Works to Simplify AI Infrastructure

GPU PaaS operates by provisioning GPU resources, ensuring tenant isolation, and offering self-service capabilities. Key technologies such as Kubernetes, SLURM, and inference pipelines are utilized to optimize GPU resource usage. Developers can experience a seamless workflow, leveraging the Rafay Platform to simplify GPU infrastructure delivery. Additionally, the platform can support CPU consumption, providing versatility for various computing needs.

The core components of GPU PaaS include efficient GPU provisioning, robust tenant isolation to ensure security, and self-service portals that empower developers and data scientists to access GPU resources without infrastructure bottlenecks. GPU PaaS leverages Kubernetes for container orchestration, SLURM for job scheduling, and inference pipelines to streamline AI/ML workloads. These technologies enable seamless integration and efficient management of GPU resources.

For example, a developer using GPU PaaS can enjoy a low code environment for AI and machine learning model training, supported by Rafay’s comprehensive management tools. The Rafay Platform collects granular chargeback information, which can be easily exported to customers’ billing systems for further use. The platform supports Terraform OpenTofu GitOps pipelines, enabling customers to deploy AI infrastructure with ease and speed.

Key Features of GPU PaaS

A high-performance GPU PaaS should have several key features, including dynamically partitioned platforms, secure multi-tenant environments, and fast paths to monetization. The Rafay Platform, for example, enables customers to convert their DGX/HGX servers into a GPU PaaS that is dynamically partitioned, secure, and multi-tenant. This allows developers and data scientists to consume GPU resources in a self-service manner, while also providing enterprise-grade cluster management and low-code environment management. Additionally, a GPU PaaS should support infrastructure as code (IaC) principles, such as Terraform, OpenTofu, and GitOps pipelines, to enable customers to programmatically manage their GPU resources. These features ensure that GPU PaaS can meet the demands of modern AI and ML workloads, providing the flexibility and scalability needed for successful deployment.

GPU PaaS vs Traditional PaaS

To better understand the differences between GPU PaaS and Traditional PaaS, here is a simple comparison table highlighting key aspects such as orchestration, workload type, and AI-readiness. This implementation allows developers and data scientists to easily access, scale, and deploy GPUs for various applications, particularly in AI, GenAI, and machine learning workloads, transforming existing GPU infrastructure into a fully operational GPU PaaS platform:

This table illustrates how GPU PaaS provides significant advantages over Traditional PaaS, particularly for developers and data scientists working on AI/ML applications. GPU PaaS offers superior orchestration, resource utilization, and AI-readiness, making it an ideal choice for high-performance GPU and CPU PaaS workloads.

Why Enterprises Need GPU PaaS Now

The growing demand for AI/ML workloads necessitates the use of GPU PaaS to optimize GPU investment with Rafay and avoid the risk of GPU sprawl. With underutilized on-prem or CSP-based GPUs, many organizations can benefit from GPU PaaS, which helps monetize your GPU investment by enabling self-service GPU consumption and cost tracking. The importance of self-service, cost tracking, and governance cannot be overstated, as GPU PaaS offers these capabilities, allowing developers to access GPU resources without delays and providing cost tracking and governance features to manage GPU usage effectively. Rafay solves for chargeback by collecting detailed chargeback information that can be exported to customer billing systems, facilitating efficient financial management and ensuring users have clarity on their resource consumption.

Without a unified platform, organizations risk GPU sprawl, leading to inefficiencies and increased costs. GPU PaaS consolidates GPU resources, providing a streamlined approach to GPU management, making it an essential tool for modern enterprises.

Top Benefits of GPU PaaS for AI and ML Workloads: Enabling Data Scientists to Access GPU Resources

GPU PaaS offers numerous benefits, including faster time-to-market for AI/ML workloads, cost and usage visibility, and developer self-service without infrastructure bottlenecks. It also ensures model portability and multi-cloud/hybrid flexibility. By accelerating AI/ML workload deployment, GPU PaaS reduces time-to-market and enhances competitive advantage. It provides multi-tenant tracking and metering, offering clear insights into GPU usage and costs, and allows developers to access GPU resources directly, eliminating infrastructure bottlenecks and enhancing productivity. Additionally, GPU PaaS supports model portability across different cloud environments, offering flexibility and scalability. This ease for AI, GenAI simplifies the process for developers and data scientists, allowing them to quickly access, scale, and deploy GPUs seamlessly.

Rafay’s comprehensive management tools are essential for effectively managing large-scale GPU clusters, emphasizing scalability, security, and operational efficiency, making them suitable for enterprise-level deployments.

Common Use Cases for GPU PaaS

GPU PaaS is ideal for various use cases, including enterprise GenAI development, training and fine-tuning LLMs, running inference at scale, and accelerating MLOps pipelines. It also supports academic research and simulations. For enterprise GenAI development, GPU PaaS provides the infrastructure needed for developing and deploying GenAI applications, enabling enterprises to innovate rapidly. It supports the training and fine-tuning of large language models (LLMs), offering the compute resources needed for these complex tasks. GPU PaaS enables large-scale inference, allowing organizations to deploy AI models efficiently and at scale, and accelerates MLOps pipelines, streamlining the process from model development to deployment. It also supports academic research and simulations, providing the compute power needed for complex scientific computations. Additionally, GPU PaaS platforms often provide AI/ML workbenches that come preconfigured and are available to users in a self-service manner.

For managing large-scale GPU clusters, Rafay’s comprehensive management tools are essential. They offer capabilities in scalability, security, and operational efficiency, making them suitable for enterprise-level deployments.

Security and Management in GPU PaaS

Security and management are critical components of a GPU PaaS. The Rafay Platform, for instance, provides comprehensive management tools that ensure scalability, security, and efficiency at an enterprise level. The platform also supports CPU consumption and can deliver a PaaS experience with CPU+GPU instances. Furthermore, the platform collects granular chargeback information that can be exported to customer billing systems, enabling customers to track their GPU usage and optimize their resource allocation. With features like GPU matchmaking, model training, and existing GPU infrastructure transformation, a GPU PaaS can provide a secure and efficient way for developers and data scientists to access and deploy GPU resources. This not only enhances productivity but also enables enterprises to monetize their GPU investment with ease, ensuring a high return on investment.

What Makes Rafay’s High Performing GPU PaaS Experience Different?

Rafay’s GPU PaaS stack stands out by offering a unique blend of features tailored to the needs of developers and data scientists. Unlike traditional platforms, Rafay delivers an enterprise-grade GPU PaaS solution that integrates seamlessly with existing infrastructure, enabling organizations to transform their GPU investments into a high-performance GPU PaaS.

By leveraging Rafay’s comprehensive management tools and infrastructure as code (IaC) automation, teams can efficiently manage GPU resources to ensure optimal usage, visibility, and cost-effectiveness. The platform supports multiple deployment options, providing flexibility and scalability across data center and cloud service provider (CSP) environments.

Rafay’s approach of using one platform for multiple deployments further enhances its versatility, making it an ideal solution for enterprises and cloud providers looking to scale and deploy GPUs efficiently. This unified approach also empowers NVIDIA cloud partners and customers to maximize their AI infrastructure investments, making Rafay the trusted choice for businesses with complex operational needs.

Getting Started with Rafay’s GPU PaaS Experience

Rafay simplifies GPU infrastructure delivery through zero-touch provisioning and preloaded environments with Rafay’s GPU PaaS stack. Rafay’s comprehensive management tools empower developers and data scientists to access GPU resources easily. Rafay offers a streamlined approach to GPU infrastructure delivery, enabling organizations to deploy GPU resources quickly and efficiently. Zero-touch provisioning and preloaded environments reduce setup time, allowing developers to focus on their core tasks. The self-service portal provides developers and data scientists with easy access to GPU resources, enhancing productivity and innovation. Explore Rafay’s GPU Cloud Workshop and learn about the collaboration with NVIDIA to deliver cutting-edge GPU solutions. Rafay supports one platform multiple deployment, offering flexibility and versatility for businesses with complex operational needs.

Conclusion

GPU PaaS is foundational for modern AI infrastructure, offering a comprehensive solution for managing GPU resources with Rafay’s comprehensive management tools. Assess your internal GPU utilization and explore Rafay’s GPU PaaS demo or trial to experience the benefits firsthand. GPU PaaS provides the infrastructure needed for AI/ML workloads, ensuring efficient GPU usage and cost management. Evaluate your current GPU utilization to identify opportunities for optimization and cost savings. Book a Rafay Platform demo or explore a trial to discover how GPU PaaS can transform your AI infrastructure. With one platform multiple deployment, Rafay’s GPU PaaS stack offers the flexibility and versatility to meet your specific business needs.

FAQ

What is GPU PaaS used for?

GPU PaaS is used for running AI, GenAI, and machine learning workloads efficiently by providing on-demand access to GPU resources. Rafay’s comprehensive management tools support model training, inference, and MLOps pipelines, making it ideal for enterprises and data scientists. This platform offers one platform multiple deployment options, allowing enterprises to choose from various deployment methods to meet their specific requirements.

How is GPU PaaS different from GPU cloud services?

GPU PaaS offers a platform as a service specifically tailored for GPU workloads, providing a fully operational environment with Rafay’s comprehensive management tools and infrastructure as code. In contrast, GPU cloud services typically provide raw GPU resources without additional platform features. This approach supports one platform multiple deployment, allowing enterprises to choose from various deployment options to meet their specific requirements.

Who needs a GPU PaaS platform?

Enterprises, developers, and data scientists who require scalable GPU resources for AI/ML workloads need a GPU PaaS platform to optimize performance, cost, and resource management. Rafay’s comprehensive management tools are essential for effectively managing large-scale GPU clusters, ensuring scalability, security, and operational efficiency. Additionally, the concept of one platform multiple deployment allows enterprises to choose from various deployment options to meet their specific requirements.

Can I run GenAI models with GPU PaaS?

Yes, GPU PaaS supports running GenAI models by offering the necessary compute resources and infrastructure, including Rafay’s comprehensive management tools, enabling efficient model training and deployment.

Additionally, GPU PaaS provides one platform multiple deployment options, allowing enterprises to choose from various deployment methods to meet their specific requirements.