Category: Generative AI
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How I am turning my one laptop into a full AI and Kubernetes lab, layer by layer
My blueprint for rebuilding my AI applications (DrJhaGPT Pro, the Neevalay bots) as a production-style platform on one 20 GB laptop: 36 layers across cluster, delivery, security, observability, AI serving, backup and cost. For every layer, what I run today, the free open-source pick for the lab, and a drop-down of the enterprise products (VKS,…
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I run a 70B model for free and own no GPU. So where does the enterprise AI stack come in?
One AI application followed end to end from a solution architect’s chair: how a RAG tool runs without a GPU, what VRAM and quantization mean, what a GPU would and would not change, why token limits disappear, where Kubernetes fits, and the sizing worksheet, BOM and three-year cost an architect has to produce. Every term…
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Leveraging Generative AI for Stock Research: A Use Case for the Non-Technical Investor
A case study in grounded GenAI: an AI research assistant for stocks and mutual funds that explains the numbers in plain English but never invents them.
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AI & GenAI — Complete Guides & Series
Deep-dive series across the AI and generative-AI stack — self-hosted, private cloud and public cloud, explained part by part. Pick one below to read the full series. Red Hat Gen AI SeriesSelf hosting generative AI on Red Hat: RHEL AI, InstructLab, Granite, OpenShift AI, KServe, vLLM, llm-d, guardrails and GPU cost, in 30 parts.Read →Infra…
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How to Reduce LLM API Costs, From Prompt Hygiene to Self Hosting
A practical, beginner to expert guide to cutting LLM API costs: token math, prompt and context hygiene, caching and batching, model routing, and when self hosting actually pays off.
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The Economics and Future of Generative AI: An Honest Take (GenAI Series, Part 30)
An honest take to close the series: why GPU utilization is the real cost lever, a blunt verdict on the hype, what is actually coming, and a recap with reading paths.
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Mixture-of-Experts and Where AI Architecture Is Heading (GenAI Series, Part 29)
Mixture-of-experts models hold enormous capacity but activate only a few experts per token, so they run cheaply. How MoE works, its memory catch, and the trends to watch.
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What It Takes to Train a Model Across Thousands of GPUs (GenAI Series, Part 28)
Training a frontier model coordinates thousands of GPUs for months. How data, tensor, pipeline and expert parallelism, the memory math, and checkpointing make it possible.
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On-Prem vs Cloud vs Hybrid for GenAI: An Honest Verdict (GenAI Series, Part 27)
Where should generative AI run? An honest framework weighing data sovereignty, the cost crossover, and control, and why most large organisations end up hybrid.
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The Network and Storage Behind Large-Scale AI (GenAI Series, Part 26)
At scale, the network between GPUs is often the real bottleneck. How NVLink, InfiniBand and RoCE, collective operations like all-reduce, and high-throughput storage keep GPUs fed.
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Scaling Inference: The Latency vs Throughput Trade-Off (GenAI Series, Part 25)
Scaling AI inference means choosing a point on the latency-versus-throughput curve. How batching, tensor and pipeline parallelism, and autoscaling on the right signal work.
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vLLM vs TensorRT-LLM vs SGLang: Which Inference Engine, and When (GenAI Series, Part 24)
The inference engine decides whether a GPU serves five users or fifty. How continuous batching and paged attention work, and when to choose vLLM, TensorRT-LLM, SGLang or NIM.
Architect’s Toolkit
PJ’s Tools
- Infra 360 Hub – All Series
- VCF 9 Interactive Walkthroughs
- VCF Design Cheatsheet
- VCF Upgrade Planner
- VCF 9 Series Hub
- VCF Deployment Hub
- AI Stack Hub
- AI Infra Sizing & Cost Calculator
- LLM & RAG Cost Calculator
- DrJhaGPT – Ask Pranay
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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