Tag: VMware Private AI
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NVIDIA AI Enterprise Explained: What’s in the Suite, How It’s Licensed, and Whether It’s Worth It
A practitioner’s breakdown of what NVIDIA AI Enterprise actually bundles, how its per-GPU licensing lands on VMware vSphere, and when the subscription earns its keep versus when you can skip it.
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VMware Private AI vs Red Hat OpenShift AI vs Hyperscaler Managed AI: An Honest Verdict (Private AI Series, Part 30)
Three ways to run enterprise inference, three very different trade-offs. A straight comparison of VMware Private AI Foundation, Red Hat OpenShift AI and hyperscaler managed AI, ending in a clear verdict.
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Disaster Recovery and Multi-Tenancy for VMware Private AI: What to Protect and How to Share (Private AI Series, Part 29)
Most of your AI platform is reproducible, a small part is not. Here is a reference design for backing up the stateful pieces of VMware Private AI and sharing GPU clusters across teams without a free-for-all.
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Guardrails and Responsible AI on VMware Private AI: What NeMo Guardrails Actually Stops (Private AI Series, Part 28)
Private does not mean safe. Here is how NeMo Guardrails wraps your models on VMware Private AI, the five rail types, and an honest line on what guardrails catch and what they do not.
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Fine-Tuning Models on VMware Private AI with NeMo Customizer: LoRA, Full SFT and When to Bother (Private AI Series, Part 27)
RAG is not always the answer. Here is how NeMo Customizer fine-tunes models on VMware Private AI, the difference between LoRA and full SFT, and an honest take on when customization beats retrieval.
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Networking for VMware Private AI Workloads: Segmentation, Ingress and the East-West Path (Private AI Series, Part 26)
Model serving lives or dies on the network nobody designed. Here is how to segment AI namespaces with NSX, expose inference endpoints through the Gateway API and the load balancer, and keep RAG east-west traffic fast and private.
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NVIDIA NIM Operator on VMware Private AI: The Reference Architecture for Declarative Model Serving (Private AI Series, Part 25)
The NIM Operator is the Kubernetes-native control plane for model serving on VMware Private AI. Here is how its CRDs, caching and autoscaling actually fit together, and the vGPU constraint that bites multi-GPU models.
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VMware Private AI Foundation Upgrade: Moving from VCF 9.0 to 9.1 Without Breaking Your GPUs (Private AI Series, Part 24)
A practical 9.0 to 9.1 upgrade runbook for VMware Private AI Foundation, plus a closing verdict on the platform after 24 parts. The order of operations, the vGPU driver branch trap, and host-by-host GPU domain remediation.
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Troubleshooting VMware Private AI Foundation: 7 Failures That Actually Bite (Private AI Series, Part 23)
The seven failures I hit most often on VMware Private AI Foundation with NVIDIA, from a dark GPU on the ESXi host to a NIM pod crashing on CUDA out of memory, with the real error strings and the checks that isolate each layer.
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VMware Private AI MLOps: Built-In Model Lifecycle vs DIY MLflow and KServe (Private AI Series, Part 22)
Two ways to run model lifecycle on VMware Private AI: the built-in Model Store and Model Runtime, or a DIY MLflow and KServe stack on VKS. Here is when each one wins, and the verdict.
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How to Benchmark LLM Inference on VMware Private AI with genai-perf (Private AI Series, Part 21)
A practical runbook for benchmarking NIM inference on VMware Private AI Foundation: the metrics that matter, the concurrency sweep that exposes the real latency-throughput curve, and how to pick an operating point you can defend.
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Is VMware Private AI Actually Private? A Security and Data Privacy Reality Check (Private AI Series, Part 20)
On-prem Private AI keeps your data in the building, but the breach risk is inside the cluster. How vDefend microsegmentation, confidential computing and RBAC in VCF 9.1 actually secure a Private AI pipeline.
Architect’s Toolkit
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
- AI Infra Sizing & Cost Calculator
- 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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