Tag: Kubernetes
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What Does Kubernetes Actually Do, and When Do You Need It? (DevOps for Beginners, Part 9)
Kubernetes basics for beginners: pods, nodes, deployments and services, how desired state and self-healing work, reading CrashLoopBackOff, and when you actually need it.
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The DevOps Lifecycle and the Toolchain Behind It (DevOps for Beginners, Part 3)
A plain map of the eight-stage DevOps lifecycle and the tools that run each stage, with a real CI pipeline you can trace from git push to live.
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Containers and Kubernetes Explained Simply (Cloud for Beginners, Part 11)
A plain-English guide to containers and Kubernetes for freshers: what a container really is, why Kubernetes exists, and what managed clusters cost on AWS, Azure and Google Cloud.
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Multi-Node LLM Training: Scheduling, Checkpointing and Fault Tolerance (NVIDIA AI Series, Part 25)
At thousands of GPUs, failures are routine. This part covers gang scheduling (Slurm vs Kubernetes vs NVIDIA Run:ai), async distributed checkpointing with NeMo, and the NVIDIA Resiliency Extension stack for fault tolerance, straggler detection, and elastic restart.
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Deploying and Autoscaling NIM in Production on Kubernetes (NVIDIA AI Series, Part 17)
How to deploy NVIDIA NIM in production using the NIM Operator and Helm, wire autoscaling on the right GPU and KV-cache signals instead of CPU, handle cold-start model load, and run blue-green rollouts without dropping throughput.
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NVIDIA Network Operator on Kubernetes: RDMA, SR-IOV, and the Accelerated Fabric (NVIDIA AI Series, Part 13)
The NVIDIA Network Operator provisions MOFED drivers, RDMA shared device plugin, SR-IOV VFs, and Multus secondary networks to Kubernetes pods. This is how GPUDirect RDMA actually works at scale on ConnectX-7 and NDR InfiniBand clusters.
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Air-Gapped Deployment, Lifecycle and CVE Patching for the NVIDIA Stack (NVIDIA AI Series, Part 15)
Running NVIDIA AI Enterprise in an air-gapped environment requires mirroring nvcr.io containers, Helm charts, and model weights before you cut the wire. Here is the branch selection, driver patch cadence, and CVE triage workflow that keeps regulated deployments defensible.
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NVIDIA GPU Operator on Kubernetes: ClusterPolicy, Components, and Day-2 Ops (NVIDIA AI Series, Part 12)
The NVIDIA GPU Operator automates every software layer a GPU node needs in Kubernetes, from kernel driver to DCGM metrics, via a single ClusterPolicy CRD. Here is what it installs, how the reconciliation loop works, when to disable the driver component, and the failure modes that will catch you on first install.
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GPU Partitioning on NVIDIA Data-Center GPUs: MIG vs vGPU vs Time-Slicing vs Passthrough (NVIDIA AI Series, Part 6)
Four ways to partition an NVIDIA H100, H200, or B200 GPU: MIG, vGPU, CUDA time-slicing, and full passthrough. This post covers the isolation guarantees, profile geometry, Kubernetes GPU Operator configuration, and a sizing worked example to help you pick the right mode for your cluster.
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Kubernetes and VKS Self-Service in VCF Automation: The All Apps Organization (VCF Automation 9 Series, Part 26)
How the All Apps organization in VCF Automation turns VKS clusters and VMs into self-service catalog items, with a real Cluster blueprint, the namespace hierarchy, and the guardrails that keep tenants honest.
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NSX 9 for Kubernetes: VKS, the Antrea-NSX Adapter and VPC Networking (NSX Series, Part 24)
VKS runs Kubernetes on VCF 9, and NSX is how its pods get networked and secured. Here is how Antrea is the default CNI, what the Antrea-NSX Adapter actually does, how VKS clusters land in NSX VPCs, and when bringing your own CNI makes sense.
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VKS Day-2 Operations: Backup, Multi-Tenancy and Capacity (VKS Series, Part 16)
Day-2 is where a VKS platform quietly succeeds or rots. Here is the hard line between infrastructure and app backup, the multi-tenancy spectrum, and the capacity math that bites late.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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