Product
Part 2: Self-Service Fractional GPU Memory with Rafay GPU PaaS
In Part-2, we show how you can provide users the means to select fractional GPU memory.
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gpu-paas
product-integrations
gpu-cloud
2025-11-02
SERVICES YOU CAN LAUNCH WITH THE RAFAY PLATFORM
Rafay-Powered Jupyter Notebooks as a Service (JNBaaS)
Rafay-powered Jupyter Notebooks as a Service (JNBaaS) allows providers and enterprises to offer governed, on-demand JupyterLab environments for data science, AI, and ML teams.Developers and researchers often face delays in provisioning GPU-backed environments or managing dependencies.
Rafay eliminates these challenges by offering instant, fully managed Jupyter notebooks equipped with pre-installed AI/ML libraries, GPU access, and built-in collaboration tools.
Service providers, enterprises, and sovereign cloud operators can deliver ready-to-use, GPU-enabled notebook workspaces that accelerate experimentation and improve productivity.
Instant Access: Launch JupyterLab notebooks immediately with no setup required
Seamless GPU Integration: Run notebooks directly on GPUs for model training, testing, and inference
AI/ML-Ready Environments: Pre-configured libraries and persistent storage simplify data preparation and prototyping
Simplify and Automate AI/ML Experimentation
Rafay automates how teams access, share, and manage GPU-powered notebook environments—removing operational friction from model development.
Pre-Configured Environments
PyTorch, TensorFlow, CUDA, and other frameworks are ready to use out of the box.
Persistent Storage
Maintain consistent access to datasets and results for reproducible workflows.
Collaboration Controls
Enable secure notebook sharing with role-based access and team visibility.
Integrated Governance
Ensure compliance, traceability, and consistent policies across all environments.
Accelerate Experimentation and Improve Resource Utilization
Move from concept to GPU-backed experimentation in seconds, accelerating AI/ML projects.
Monetize notebooks as value-added SKUs to increase utilization per GPU.
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Enable secure, multi-user access with reproducibility and version control.
Deliver compliant, notebook services aligned with data residency and governance requirements
Featured Resources
Operationalizing AI Fabrics with Aviz ONES, NVIDIA Spectrum-X, & Rafay
Aviz ONES, NVIDIA Spectrum-X Ethernet, and the Rafay Platform deliver a unified solution turning GPU fabrics into governed, cloud-native environments, instantly consumable.
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GPU PaaS Reference Architecture with NVIDIA
Enterprises and Cloud Providers leverage the Rafay Platform to deliver self-service consumption of AI and cloud-native infrastructure to developers and data scientists.
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Unlock Your AI Potential with Cisco and Rafay: Transform AI PODs into a Self-Service GPU Cloud
Cisco provides AI-optimized infrastructure. The Rafay Platform makes it usable across teams, tenants, and use cases in days.
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The CIO’s guide to scalable, compliant, and developer-ready AI deployment
Learn how the Rafay Platform "allows CIOs to align their AI strategies with national regulatory frameworks while maintaining global scalability and agility" in this analyst report.
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Rafay Named Outperformer in 2025 GigaOm Radar Report for Managed Kubernetes
The latest Radar Report from GigaOm, the Rafay Platform's "Managed Kubernetes" capability is ranked as an “Outperformer."
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Gartner® Report – Market Trend: CSPs’ Opportunity to Capitalize on AI Infrastructure Through GPU as a Service
CSPs can harness these strategic opportunities to capitalize on the AI-optimized IaaS market, projected at $80 billion in 2028.
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Building AI Value within Borders
“Rafay’s central orchestration platform facilitates efficient, self-service infrastructure and AI application management” writes Accenture | NVIDIA in 2025 Building AI Value Within Borders paper.
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GPU Cloud Evaluation Report
Learn how to accelerate the ROI of your GPU infrastructure by quickly delivering a fully functional GPU Cloud with the self-service workflows and infrastructure orchestration of the Rafay Platform.
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How Enterprise Platform Teams Can Accelerate AI/ML Initiatives
This paper explores the key challenges that organizations experience supporting these initiatives, as well as best practices for successfully leveraging Kubernetes to accelerate AI/ML projects.
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"We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay"
"We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay"
"We are able to deliver new, innovative products and services to the global market faster and manage them cost-effectively with Rafay"
Most Recent Blogs
Product
Self-Service Fractional GPUs with Rafay GPU PaaS
This is Part-1 in a multi-part series on end user, self service access to Fractional GPU based AI/ML resources.
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2025-11-01
Product
Unlock the Next Step: From Cisco AI PODs to Self-service GPU Clouds with Rafay
Read Now
ai-infrastructure
enterprises
gpu-cloud
gpu-paas
2025-10-20
Try the Rafay Platform for Free
See for yourself how to turn static compute into self-service engines. Deploy AI and cloud-native applications faster, reduce security & operational risk, and control the total cost of Kubernetes operations by trying the Rafay Platform!
