{"id":28,"date":"2026-03-10T09:00:00","date_gmt":"2026-03-10T03:30:00","guid":{"rendered":"https:\/\/rdp.in\/blog\/?p=28"},"modified":"2026-04-21T18:41:03","modified_gmt":"2026-04-21T13:11:03","slug":"building-your-ai-factory-in-india-a-cios-playbook-for-2026","status":"publish","type":"post","link":"https:\/\/rdp.in\/blog\/building-your-ai-factory-in-india-a-cios-playbook-for-2026\/","title":{"rendered":"Building Your AI Factory in India: A CIO’s Playbook for 2026"},"content":{"rendered":"\n

Part 2 of 3 \u00b7 RDP AI Infrastructure Series<\/strong><\/em><\/p>\n\n\n\n

In Part 1 of this series, we made the case for why<\/em> Indian enterprises are repatriating AI workloads from cloud to on-prem. This post is about how<\/em> to actually do it \u2014 without overbuilding, underbuilding, or designing yourself into a corner.<\/p>\n\n\n\n

An AI factory is not a single product. It\u2019s a stack: compute, networking, storage, power, cooling, software, and the people who run it. Get the architecture right and the deployment compounds for five years. Get it wrong and you end up with a very expensive rack that trains one model per quarter.<\/p>\n\n\n\n

Here\u2019s how we think about it at RDP \u2014 the questions we walk every customer through, and the decisions that tend to matter most.<\/p>\n\n\n\n

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Start with the Workload, Not the Hardware<\/h2>\n\n\n\n

The single most common mistake we see: organizations start by asking \u201chow many H100s do we need?\u201d<\/p>\n\n\n\n

Wrong question. Start with the workload.<\/p>\n\n\n\n

There are three broad workload shapes, and each wants a different architecture:<\/p>\n\n\n\n

Inference-dominant.<\/strong> You\u2019re running trained models in production \u2014 a chatbot, a document classifier, a fraud detector, a RAG pipeline over corporate data. The GPU is usually one of the smaller SKUs (L40S, H100 PCIe, or even A100 for cost-optimized inference). You need throughput, low latency, and high uptime. Networking is less critical \u2014 inference workloads rarely need RDMA-class fabrics. Storage is moderate.<\/p>\n\n\n\n

Fine-tuning-dominant.<\/strong> You\u2019re taking open-weight or commercial base models and adapting them to your data. Think LoRA and QLoRA on Llama, Mistral, or domain-specific models. You need a mid-size GPU cluster (4\u201316 H100-class), fast local NVMe, and good-but-not-extreme networking. This is the sweet spot for most Indian mid-enterprise deployments.<\/p>\n\n\n\n

Pre-training or large-scale training.<\/strong> You\u2019re building foundation models from scratch, or doing full fine-tunes at meaningful scale. This needs real infrastructure \u2014 32 to 128+ GPUs, 400Gb\/s InfiniBand or equivalent, liquid cooling, shared parallel file systems. This is AI Factory territory in the true sense, and it\u2019s where the top of the Indian market \u2014 banks, telcos, research institutions, and sovereign AI initiatives \u2014 is investing.<\/p>\n\n\n\n

The honest truth:<\/strong> 85% of Indian enterprise deployments are inference-plus-fine-tuning. Very few need pre-training infrastructure. Don\u2019t buy for the glamour workload; buy for the workload you actually run.<\/p>\n\n\n\n

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The Five Decisions That Matter<\/h2>\n\n\n\n

Once you know your workload shape, there are five architectural decisions that drive everything else. Get these right and the rest is execution.<\/p>\n\n\n\n

1. GPU SKU and count<\/h3>\n\n\n\n

The hardware conversation has narrowed. For Indian enterprises in 2026, the practical choices are:<\/p>\n\n\n\n