# Mohan Atreya

## One-Click Digital Twins: Deploying the NVIDIA Omniverse DSX Blueprint using Rafay  
See how Rafay transforms the NVIDIA Omniverse DSX Blueprint into a one-click, self-service digital twin offering with governed GPU access, multi-tenancy, and automated session management.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/one-click-digital-twins-deploying-the-nvidia-omniverse-dsx-blueprint-using-rafay)

## NUMA-Aware GPU VMs: How NVIDIA's Reference Architecture Actually Fixes It  
Learn how NVIDIA's NUMA-aware GPU VM reference architecture improves GPU performance by aligning CPUs, memory, and GPUs to reduce latency and maximize throughput.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/numa-aware-gpu-vms-how-nvidias-reference-architecture-actually-fixes-it)

## Private Token Factories: How Rafay and Protopia AI Let Sensitive Workloads Run on Shared GPU Capacity  
Rafay and Protopia AI eliminate plaintext exposure, letting regulated enterprises finally run sensitive workloads on shared GPU inference infrastructure.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/private-token-factories-how-rafay-and-protopia-ai-let-sensitive-workloads-run-on-shared-gpu-capacity)

## Why is your GPU VM slower Than Its Twin?  
Two identically configured GPU VMs can deliver very different performance because the GPU, CPU cores, and memory may not be placed on the same NUMA node  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/why-is-your-gpu-vm-slower-than-its-twin-p)

## Serving LLMs on Arm: Running Rafay Token Factory on NVIDIA DGX Spark  
Learn how Rafay Token Factory turns NVIDIA DGX Spark into a managed, multi-tenant LLM serving endpoint with Arm-native Kubernetes, metering, governance, and OpenAI-compatible API access.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/serving-llms-on-arm-running-rafay-token-factory-on-nvidia-dgx-spark)

## Automated GPU Health Monitoring with NVIDIA NVSentinel on the Rafay Platform  
Every GPU node monitored. Faulty nodes automatically quarantined and remediated. The Rafay Platform and NVIDIA NVSentinel make that a fleet-wide guarantee, not a per-cluster aspiration.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/automated-gpu-health-monitoring-with-nvidia-nvsentinel-on-the-rafay-platform)

## AI Factories Will Be Won on Efficiency: Why the Rafay + Kubex Partnership Matters  
GPU costs are rising. Workloads are unpredictable. Platform teams are stretched. The next frontier of enterprise AI is operating efficiently at scale. That is the problem Rafay and Kubex are solving together.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/ai-factories-will-be-won-on-efficiency-why-the-rafay-kubex-partnership-matters)

## Compute Domains: Bringing Multi-Node NVLink Awareness to Kubernetes  
Learn how compute domains and multi-node NVLink enable high-performance, distributed GPU workloads in Kubernetes, improving scalability, resource utilization, and AI infrastructure efficiency.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/compute-domains-bringing-multi-node-nvlink-awareness-to-kubernetes)

## NVIDIA Dynamo: Turning Disaggregated Inference Into a Production System  
Discover how NVIDIA Dynamo turns disaggregated inference into a production-ready system, enabling scalable, efficient AI services with better resource utilization and operational control.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/nvidia-dynamo-turning-disaggregated-inference-into-a-production-system)

## Introduction to Disaggregated Inference: Why It Matters  
Learn how disaggregated inference improves GPU utilization, scalability, and cost efficiency by separating compute, memory, and serving layers—enabling more flexible, self-service AI infrastructure.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/introduction-to-disaggregated-inference-why-it-matters)

## Fine Tuning as a Service using Rafay and Unsloth Studio  
Turn Unsloth Studio into a repeatable, app store style fine tuning experience with Rafay, enabling one click deployment without Kubernetes or MLOps complexity.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/fine-tuning-as-a-service-using-rafay-and-unsloth-studio)

## From Docker Image to 1-Click App: Enabling Self-Service for Custom Apps  
Turn Docker images into 1-click, self-service apps, securely delivered across multi-tenant Kubernetes environments with built-in governance and control.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/from-docker-image-to-1-click-app-enabling-self-service-for-custom-apps)

## Adding New Language Support to the Self Service Portal in 5 Mins  
Add any language to your Self-Service Portal in minutes. Deliver a localized, frictionless experience for global AI teams with Rafay.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/adding-new-language-support-to-the-self-service-portal-in-5-mins)

## OpenClaw on Kubernetes: A Platform Engineering Pattern for Always-On AI  
A deep dive into OpenClaw as a gateway-centric AI runtime and how platform teams can deploy, secure, and scale it as a governed service on Kubernetes.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/openclaw-on-kubernetes-a-platform-engineering-pattern-for-always-on-ai)

## Developer Pods for Platform Teams: Designing the Right Self-Service GPU Experience  
A deep dive into how platform teams use SKU design to transform raw GPU infrastructure into intuitive, self-service Developer Pod experiences for AI builders.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/developer-pods-for-platform-teams-designing-the-right-self-service-gpu-experience)

## Developer Pods: A Self-Service GPU Experience That Feels Instant  
From a simple form to a live SSH session in ~30 seconds. This is what self-service GPU access actually looks like with Rafay Developer Pods, no Kubernetes knowledge required.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/developer-pods-a-self-service-gpu-experience-that-feels-instant)

## Instant Developer Pods: Rethinking GPU Access for AI Teams  
Rafay Developer Pods eliminate the ticket queues and bloated VMs holding AI teams back — GPU-ready in ~30 seconds, Kubernetes-powered, complexity-free.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/instant-developer-pods-rethinking-gpu-access-for-ai-teams)

## Scaling Trust: The Fortanix and Rafay Integration for Enterprise Confidential AI  
Learn how the Fortanix and Rafay integration enables confidential AI for enterprises—protecting sensitive data while running AI workloads on secure, governed GPU platforms.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/scaling-trust-the-fortanix-and-rafay-integration-for-enterprise-confidential-ai)

## NVIDIA AICR Generates It. Rafay Runs It. Your GPU Clusters, Finally Under Control  
NVIDIA AI Cluster Runtime (AICR) simplifies AI infrastructure deployment. Learn how Rafay operationalizes GPU clusters with governance, self-service access, and platform automation.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/nvidia-aicr-generates-it-rafay-runs-it-your-gpu-clusters-finally-under-control)

## Run nvidia-smi on Remote GPU Kubernetes Clusters Using Rafay Zero Trust Access  
See how infrastructure operators can securely validate GPU health in remote Kubernetes clusters by running nvidia-smi using Rafay’s Zero Trust Kubectl Access workflow.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/run-nvidia-smi-on-remote-gpu-kubernetes-clusters-using-rafay-zero-trust-access)

## How Rafay Helps GPU Clouds Run Complex Hackathons at Scale  
Discover how Rafay enables GPU cloud providers to run large-scale hackathons by instantly provisioning secure, ready-to-use GPU developer environments for thousands of participants.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/how-rafay-helps-gpu-clouds-run-complex-hackathons-at-scale)

## How GPU Clouds Deliver NVIDIA Run:ai as Self-Service with Rafay GPU PaaS  
Learn how Rafay GPU PaaS enables GPU Clouds to offer NVIDIA Run:ai as a fully automated, multi-tenant managed service delivered through self-service with lifecycle management and turnkey deployment.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/how-gpu-clouds-deliver-nvidia-run-ai-as-self-service-with-rafay-gpu-paas)

## GPU Cloud Billing: From Usage Metering to Billing  
In this blog, we take the next step toward a complete billing workflow—automatically transforming usage into billable cost using SKU-specific pricing.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/gpu-cloud-billing-from-usage-metering-to-billing)

## Goodbye to Ingress NGINX – What Happens Next?  
The community Ingress NGINX project is entering end-of-life in March 2026. Discover what this means for Kubernetes users and why you’ll need to migrate, what alternatives exist (Gateway API, Traefik, etc.), and how to plan your transition smoothly with minimal disruption.  
[Read Now](https://info@rafay.co/ai-and-cloud-native-blog/goodbye-to-ingress-nginx---what-happens-next)