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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.

Drift Prevention vs Detection: Does a Polling Approach make sense At Scale?

Learn about Rafay's approach to drift prevention and detection in this blog.

Unlock the Next Step: From Cisco AI PODs to Self-service GPU Clouds with Rafay

GPU/Neocloud Billing using Rafay’s Usage Metering APIs

Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption.

What is Agentic AI?

Agentic AI is the next evolution of artificial intelligence—autonomous AI systems composed of multiple AI agents that plan, decide, and execute complex tasks with minimal human intervention.

Deep Dive into nvidia-smi: Monitoring Your NVIDIA GPU with Real Examples

Whether you’re training deep learning models, running simulations, or just curious about your GPU’s performance, nvidia-smi is your go-to command-line tool.

GigaOm: CEO SPEAKS interview with Rafay CEO Haseeb Budhani

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.

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.

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.

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.

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.