The GPU Cloud Solution integrates Rafay’s platform with NVIDIA’s accelerated computing infrastructure to deliver scalable GPU PaaS capabilities across cloud and on-premises environments.
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GPU Cloud Solution: Empowering AI Workloads with NVIDIA and Rafay
63%
Lower cloud costs
4x
More frequent deployments
76%
Lower MTTR
The GPU Cloud Solution, powered by Rafay and distributed by CSC Distribution, leverages NVIDIA’s accelerated computing infrastructure to deliver a Platform-as-a-Service (PaaS) experience for GPU-intensive workloads. This solution enables cloud providers and enterprises to offer developers and data scientists seamless access to GPU resources for AI, ML, and generative AI applications
Targeted Customers
The GPU Cloud Solution serves industries and roles requiring high-performance GPU resources for AI and ML workloads:
Cloud Providers (NVIDIA Cloud Partners): Deliver GPU PaaS to customers with white-labeled or API-integrated portals.
Enterprises with Internal GPU Clouds: Build private GPU infrastructure for internal AI/ML teams.
AI/ML Developers & Data Scientists: Access GPU resources on-demand for training and inference.
Technology Companies: Deploy AI-driven applications like generative AI and real-time analytics.
Healthcare & Life Sciences: Process large-scale genomic data with GPU-accelerated pipelines.
Media & Entertainment: Support AI-driven content creation and rendering workflows.
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High-Level Design & Components
Key Components
This High-Level Design diagram illustrates an AI/HPC infrastructure architecture built with NVIDIA-certified hardware, Rafay Kubernetes automation, and multi-tiered storage.
Rafay Platform
- Rafay Controller: Centralized control plane managing infrastructure.
- Rafay Agent: Deployed on BCM Nodes to enforce policies and manage lifecycle.
- Rafay Operator: Manages vClusters within Kubernetes.
Compute Infrastructure
- vCluster Virtual Clusters: Hosted on NVIDIA Certified Hardware.
- Bare Metal: Dedicated GPU nodes for performance-intensive workloads.
- All compute nodes are part of the Kubernetes Cluster(s).
Networking
- Access & Storage Network: Core, spine, and leaf fabric for control/data traffic.
- RDMA Network: Low-latency network for GPU-to-storage and GPU-to-GPU traffic.
Key Features & Capabilities
Self-Service GPU Access for Developers
Self-service SKUs for bare metal, K8s, virtual clusters, and fractional GPUs with on-demand templates.
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Turnkey NVIDIA Services Integration
One-click deploy of NIM, NVCF, Run\:AI with NVIDIA AI Enterprise support for scalable, secure AI.
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Resource Management & Cost Efficiency
Resource quotas, usage metrics, and automated SKUs for chargeback and custom GPU packages.
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Kubernetes Cluster Lifecycle Management
Unified management for CNCF K8s, EKS, AKS, GKE with GitOps-based fleet operations at scale.
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Testimonials & Case Studies
Healthcare AI Acceleration
- Challenge: A healthcare firm needed scalable GPU resources for genomic sequencing analysis.
- Solution: Deployed a GPU Kubernetes cluster with NVIDIA NIM integration using this solution.
- Results: Reduced analysis time by 60% and improved data scientist productivity by 40%.
Media Content Creation
- Challenge: A media company required GPU resources for AI-driven video rendering across regions.
- Solution: Leveraged virtual GPU clusters and Run:AI for fractional GPU allocation.
- Results: Cut rendering costs by 35% and accelerated project delivery by 50%.
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