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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.
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Air-Gapped VMware Private AI Foundation: Mirroring, AMT and the Bootstrap Problem (Private AI Series, Part 19)
Deploying VMware Private AI Foundation in a fully disconnected enclave: what to mirror, how the artifact mirroring tool (AMT) fits, the Harbor bootstrap problem, and how to validate offline NIM and GPU before handover.
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VMware Private AI Sizing and Cost: GPU Memory Math, Capacity Planning and TCO (Private AI Series, Part 18)
How to size a VMware Private AI platform from the workload up: GPU memory math, the KV cache trap, a model-to-card matrix, and the four-layer cost model that actually decides the business case.
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GPU Monitoring with VCF Operations for VMware Private AI: The Signals That Actually Catch a Failing Workload (Private AI Series, Part 17)
VCF Operations gives you GPU dashboards out of the box, but the metric most teams trust is the one that lies. Here is what to watch on a Private AI Foundation estate, why GPU utilization misleads, and the hardware-health signals the default dashboards never surface.
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Self-Service AI Catalog Items with VCF Automation for VMware Private AI (Private AI Series, Part 16)
How to publish self-service GPU catalog items for VMware Private AI Foundation with the VCF Automation Quickstart, plus the namespace, vGPU class and quota bindings that decide whether the catalog is safe to hand out.
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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.
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Deep Learning VMs in VMware Private AI Foundation: The Data Scientist Workbench (Private AI Series, Part 10)
What a Deep Learning VM in VMware Private AI Foundation actually is, how the image is built, the first-boot steps that quietly break deployments, and when to move off it to a VKS cluster.
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.
Recommendations & Endorsements
Pranay has very deep technical knowledge and exceptional capabilities. He has strong ability to well-manage complex situations and deliver the customer expectations with high standards of satisfaction. We worked together in one of most complex migration projects and Pranay delivered flawlessly.
Pranay was my VMware Architect on one of our financial accounts at IBM. He was instrumental in documenting and recommending our reference architecture. As a Chief Architect, you are only as good as your team that supports you. Pranay was top notch.
Pranay is proactive, result oriented, responsible and technically sound. Always ready to put all his energy to get the job done. Exceptional troubleshooting skills in Windows Server, virtualization and VMware — a great asset to any company.
Pranay is one of the most talented and dedicated persons I have ever worked with. His vast knowledge from different domains enables him to discuss and help on almost every topic in IT. Needless to say, he is expert of experts in VMware.
DrJha