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Serving LLMs on Arm: Running Rafay Token Factory on NVIDIA DGX Spark
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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.

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Automated GPU Health Monitoring with NVIDIA NVSentinel on the Rafay Platform
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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.

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AI Factories Will Be Won on Efficiency: Why the Rafay + Kubex Partnership Matters
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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.

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Compute Domains: Bringing Multi-Node NVLink Awareness to Kubernetes
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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.

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NVIDIA Dynamo: Turning Disaggregated Inference Into a Production System
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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.

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Introduction to Disaggregated Inference: Why It Matters
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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.

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Fine Tuning as a Service using Rafay and Unsloth Studio
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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.

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From Docker Image to 1-Click App: Enabling Self-Service for Custom Apps
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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.

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Adding New Language Support to the Self Service Portal in 5 Mins
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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.

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OpenClaw on Kubernetes: A Platform Engineering Pattern for Always-On AI
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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.

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Developer Pods for Platform Teams: Designing the Right Self-Service GPU Experience
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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.

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Developer Pods: A Self-Service GPU Experience That Feels Instant
Product

Developer Pods: A Self-Service GPU Experience That Feels Instant

rom 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.

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Instant Developer Pods: Rethinking GPU Access for AI Teams
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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.

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Scaling Trust: The Fortanix and Rafay Integration for Enterprise Confidential AI

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.

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NVIDIA AICR Generates It. Rafay Runs It. Your GPU Clusters, Finally Under Control
Product

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.

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Run nvidia-smi on Remote GPU Kubernetes Clusters Using Rafay Zero Trust Access
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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.

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How Rafay Helps GPU Clouds Run Complex Hackathons at Scale
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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.

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How GPU Clouds Deliver NVIDIA Run:ai as Self-Service with Rafay GPU PaaS
Product

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.

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GPU Cloud Billing: From Usage Metering to Billing
Product

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.

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Goodbye to Ingress NGINX – What Happens Next?
Product

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.

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Deploy Workload using DRA ResourceClaim in Kubernetes
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Deploy Workload using DRA ResourceClaim in Kubernetes

In this second blog, we installed a Kuberneres v1.34 cluster and deployed an example DRA driver on it with "simulated GPUs". In this blog, we’ll will deploy a few workloads on the DRA enabled Kubernetes cluster to understand how "Resource Claim" and "ResourceClaimTemplates" work.

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GPU/Neocloud Billing using Rafay’s Usage Metering APIs

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.

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

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.

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Introduction to Dynamic Resource Allocation (DRA) in Kubernetes

Introduction to Dynamic Resource Allocation (DRA) in Kubernetes

In this post, we’ll look at how a new GA feature in Kubernetes v1.34 — Dynamic Resource Allocation (DRA) — aims to solve these problems and transform GPU scheduling in Kubernetes.

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Telus
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Firmus
Buzz HPC
Indosat
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Moneygram
Ooredoo
Era4
Palo Alto Networks
Software
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