Author

Mohan Atreya

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

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

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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Rethinking GPU Allocation in Kubernetes

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

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Understanding ArgoCD Reconciliation: How It Works, Why It Matters, and Best Practices

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

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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.

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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.

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Custom GPU Resource Classes in Kubernetes

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

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The Rise of AI Agents: From Zero to Production

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Product

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.

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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.

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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.

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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.

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Optimizing AI Workloads for Multi-Cloud Environments with Rafay and GPU PaaS

Rafay’s platform enables you build a GPU PaaS for AI workloads so you can confidently operate machine learning models, generative AI, and neural networks at scale. It orchestrates your hybrid and multi-cloud computing resources, improves operational flexibility, and includes precise governance controls to support continual compliance.

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Operationalizing AI: Solutions to Machine Learning Workflow Automation Challenges

Machine learning (ML) has emerged as a transformative force, enabling organizations to derive critical insights, enhance customer experiences, and make data-driven predictions. However, operationalizing machine learning workflows presents significant challenges, especially for enterprises with complex, cloud-based infrastructures.

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Achieving Optimal AI Performance with Tuning-as-a-Service

Tuning-as-a-Service (another TaaS but not to be confused with Training-as-a-service) is a cloud-based solution that optimizes AI models by automating the adjustment of hyperparameters to enhance model accuracy, efficiency, and overall performance. By leveraging advanced algorithms and scalable cloud resources, TaaS platforms streamline the often-complex and time-consuming process of model tuning , allowing organizations to achieve optimal AI performance without dedicating extensive manual effort.

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Optimizing AI Deployments with Training-as-a-Service Platforms

As artificial intelligence continues to reshape industries, the demand for efficient, scalable training solutions has surged. Training-as-a-Service (TaaS) platforms are emerging as essential tools for developers, data architects, and platform engineering teams working in AI model development.

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Mohan Atreya

GPU/Neocloud Billing using Rafay’s Usage Metering APIs

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

Introduction to Dynamic Resource Allocation (DRA) in Kubernetes

Rethinking GPU Allocation in Kubernetes

Understanding ArgoCD Reconciliation: How It Works, Why It Matters, and Best Practices

Choosing the Right Fractional GPU Strategy for Cloud Providers

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

Custom GPU Resource Classes in Kubernetes

The Rise of AI Agents: From Zero to Production

Configure and Manage GPU Resource Quotas in Multi-Tenant Clouds

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

Self-Service Slurm Clusters on Kubernetes with Rafay GPU PaaS

Project Slinky: Bringing Slurm Scheduling to Kubernetes

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

Powering GPU Cloud Billing: Rafay + Monetize360 Integration

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

Get Started with BioContainers using Rafay

BioContainers: Streamlining Bioinformatics with the Power of Portability

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

GPU PaaS™ Unleashed: Empowering Platform Teams to Drive Innovation

Optimizing AI Workloads for Multi-Cloud Environments with Rafay and GPU PaaS

Operationalizing AI: Solutions to Machine Learning Workflow Automation Challenges

Achieving Optimal AI Performance with Tuning-as-a-Service

Optimizing AI Deployments with Training-as-a-Service Platforms

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