Mohan Atreya

Goodbye to Ingress NGINX – What Happens Next?

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.

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.

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.

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.

Rethinking GPU Allocation in Kubernetes

Kubernetes has cemented its position as the de-facto standard for orchestrating containerized workloads in the enterprise.

GitOps Without Borders: Running Argo CD Across Isolated Security Domains with Rafay’s Zero-Trust Kubectl

In this blog, we will describe how Rafay Zero Trust Kubectl Access Proxy gives Argo CD a secure path to every cluster in the fleet, even when those clusters sit deep behind corporate firewalls.

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.

ArgoCD Reconciliation Explained: How It Works and Why It Matters

ArgoCD is a powerful GitOps controller for Kubernetes, enabling declarative configuration and automated synchronization of workloads.

Choosing the Right Fractional GPU Strategy for Cloud Providers

As demand for GPU-accelerated workloads soars across industries, cloud providers are under increasing pressure to offer flexible, cost-efficient, and isolated access to GPUs.

Demystifying Fractional GPUs in Kubernetes: MIG, Time Slicing, and Custom Schedulers

As GPU acceleration becomes central to modern AI/ML workloads, Kubernetes has emerged as the orchestration platform of choice.

Custom GPU Resource Classes in Kubernetes

In the modern era of containerized machine learning and AI infrastructure, GPUs are a critical and expensive asset.

What Are AI Agents and How Do They Work?

Artificial Intelligence (AI) has moved far beyond simple chat bots and rigid automation. At the frontier of this evolution lies a powerful new paradigm : AI Agents.

Configure and Manage GPU Resource Quotas in Multi-Tenant Clouds

In multi-tenant GPU cloud environments, effective resource management is critical to ensure fair usage and prevent contention.

Slash EKS Cluster Costs by 20-30% Instantly with AWS Graviton

If you’re running Kubernetes workloads on Amazon EKS backed by Intel-based instances, you’re leaving significant savings on the table.

Self-Service Slurm Clusters on Kubernetes with Rafay GPU PaaS

Project Slinky and Rafay’s GPU Platform-as-a-Service (PaaS) combined provide enterprises and cloud providers with a transformative combination that enables secure, multi-tenant, self-service access to Slurm-based HPC environments on shared Kubernetes clusters.

Project Slinky: Bringing Slurm Scheduling to Kubernetes

As high-performance computing (HPC) environments evolve, there’s an increasing demand to bridge the gap between traditional HPC job schedulers and modern cloud-native infrastructure.

Using Cilium as a Kubernetes Load Balancer: A Powerful Alternative to MetalLB

In Kubernetes, exposing services of type LoadBalancer in on-prem or bare-metal environments typically requires a dedicated “Layer 2” or “BGP-based” software load balancer—such as MetalLB.

Powering GPU Cloud Billing: Rafay + Monetize360 Integration

In the fast-evolving world of GPU cloud services and AI infrastructure, accurate, flexible, and real-time billing is no longer optional — it’s mission critical.

Cost Management for SageMaker AI: The Case for Strong Administrative Guardrails

Enterprises are increasingly leveraging Amazon SageMaker AI to empower their data science teams with scalable, managed machine learning (ML) infrastructure.

Get Started with BioContainers using Rafay

In this step-by-step guide, the Bioinformatics data scientist will use Rafay’s end user portal to launch a well resourced remote VM and run a series of BioContainers with Docker.

BioContainers: Streamlining Bioinformatics with the Power of Portability

In today’s fast-paced world of bioinformatics, the constant evolution of tools, dependencies, and operating system environments presents a significant challenge.

GPU PaaS™ (Platform-as-a-Service) for AI Inference at the Edge: Revolutionizing Multi-Cluster Environments

Enterprises are turning to AI/ML to solve new problems and simplify their operations, but running AI in the datacenter often compromises performance. Edge inference moves workloads closer to users, enabling low-latency experiences with fewer overheads, but it's traditionally cumbersome to manage GPUs (Graphics Processing Units) in distributed infrastructure.

GPU PaaS™ Unleashed: Empowering Platform Teams to Drive Innovation

GPUs underpin cutting-edge AI, machine learning, and big data workloads. They also provide critical acceleration for simulation, video rendering, and streaming tasks.