Category: AI/ML
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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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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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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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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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VMware Private AI Agent Builder: Composing Models, Knowledge Bases and Prompts (Private AI Series, Part 15)
Agent Builder in VMware Private AI Services lets you compose a model endpoint, a knowledge base and prompt instructions into a grounded agent. Here is what it actually does, where it sits, and where the agentic hype gets ahead of reality.
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Building a RAG Pipeline on VMware Private AI: 7 Failures That Quietly Break Retrieval (Private AI Series, Part 14)
Most RAG failures on VMware Private AI Foundation are not the LLM. Here are the seven pipeline failures that quietly wreck retrieval quality on PAIF 9, and how I fix each one in the field.
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Vector Databases in VMware Private AI: Running pgvector on Data Services Manager (Private AI Series, Part 13)
A reference-architecture look at the retrieval tier of VMware Private AI: where DSM-managed PostgreSQL with pgvector sits, how to place and size it, and whether to index with HNSW or IVFFlat.
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VMware Private AI Services: Deploying Models with the Model Store and Model Runtime (Private AI Series, Part 12)
A hands-on runbook for Private AI Services 2.1: stand up a Harbor model gallery, validate and push models with the vcf pais CLI, then serve them as endpoints through Model Runtime and the ML API Gateway.
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NVIDIA NIM Microservices on VMware Private AI: The Model-Serving Layer Explained (Private AI Series, Part 11)
NVIDIA NIM is the model-serving layer of VMware Private AI. A reference-architecture look at the NIM Operator, NIMCache and NIMService, GPU placement, and the design choices that decide whether your endpoints survive production.
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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