GPU PaaS

Launch a GPU PaaS
in Days

Accelerate your time-to-market with high-value NVIDIA
hardware by rapidly launching a PaaS for GPU consumption

Deliver self-service consumption of GPU-based hardware to your developers and data scientists

Manage GPU environments as a singular pool of resources for high utilization

Effortlessly manage and optimize GPUs across multiple racks in your data center or across CSP environments

Reduce resource wastage with GPU matchmaking and GPU virtualization

Dynamically match end-user needs with the optimal environment based on proximity, cost efficiency, GPU type, etc. Virtualize GPUs for sub-GPU sharing to get the most out of your GPU hardware

Provide a “storefront” experience to users needing GPUs

Let developers and data scientists select from an array of preconfigured GPU workspaces on demand, sized by the number of GPUs, cores, amount of memory, and storage options

Offer pre-configured AI workspaces through GPU storefront

Supply pre-configured workspaces for AI model development, training and serving/inferencing with all required AI tools, such as Jupyter Notebooks and model registries, so your data scientists can be productive quickly

Simultaneously support hundreds of teams with out-of-the-box multi-tenancy capabilities

Leverage Rafay’s robust, multi-tenancy capabilities to support hundreds of internal or external customers from a single pane of glass. Each customer operates as a separate tenant, ensuring data isolation and security, while supporting multiple users within each environment

Centralize policy definition and enforcement across your AI infrastructure

Take charge of your limited GPU resources with our comprehensive administrative tools designed for service providers and enterprise IT. Enforce policies that control usage and prevent wastage, ensuring that your resources are used efficiently and effectively

Drive Business Growth with Rapid GPU PaaS Deployment

With Rafay, companies bridge the utilization gap between AI hardware and their AI development to realize the following benefits:

Launch a GPU
PaaS in Days

Outpace the competition by rapidly launching a GPU PaaS to service thousands of customers in days, not months or years

Harness the Power
of AI Faster

Complex processes and steep learning curves shouldn’t prevent developers and data scientists from building, training and tuning their AI-based applications. A turnkey MLOps toolset that is offered as a service enables customers to be more productive without worrying about infrastructure details

Maximize Your Investment in AI Infrastructure

Increase utilization of your accelerated computing hardware investment by utilizing capabilities such as GPU virtualization and matchmaking, resulting in improved margins and happier customers

Download the White Paper
Scale AI/ML Adoption

Delve into best practices for successfully leveraging Kubernetes and cloud operations to accelerate AI/ML projects.

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Most Recent Blogs

Image for GPU PaaS Unleashed: Empowering Platform Teams to Drive Innovation

GPU PaaS Unleashed: Empowering Platform Teams to Drive Innovation

December 18, 2024 / by Mohan Atreya

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… Read More

Image for Optimizing AI Workloads for Multi-Cloud Environments with Rafay and GPU PaaS

Optimizing AI Workloads for Multi-Cloud Environments with Rafay and GPU PaaS

November 27, 2024 / by Mohan Atreya

Rafay’s platform enables you build a GPU PaaS for AI workloads so you can confidently operate machine learning models, generative AI, and neural networks at scale. It orchestrates your hybrid and multi-cloud computing resources, improves operational flexibility, and… Read More

Image for Operationalizing AI: Solutions to Machine Learning Workflow Automation Challenges

Operationalizing AI: Solutions to Machine Learning Workflow Automation Challenges

November 15, 2024 / by Mohan Atreya

Machine learning (ML) has emerged as a transformative force, enabling organizations to derive critical insights, enhance customer experiences, and make data-driven predictions. However, operationalizing machine learning workflows presents significant challenges, especially for enterprises with complex, cloud-based infrastructures. Machine… Read More

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