Category: Generative AI
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Attention Is All You Need: the 2017 paper that changed AI strategy and the IT industry
How the 2017 Transformer paper made AI parallel, turned AI budgets into GPU, memory, network and power decisions, and changed research, business, jobs and the wider IT industry.
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How to become an AI engineer: an end-to-end guide with free resources
Six stages, from Python and maths to building, fine-tuning and running LLM applications in production, with the free courses, playlists and books for each stage and a project to build at every step.
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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.
Architect’s Toolkit
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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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