AI Infrastructure Buying Guide for CIOs (2026)

Buy AI infrastructure by working backward from the workload, not the hardware. Decide four things in order: (1) what you're running (model size + training vs inference) → this sets GPU count and memory; (2) on-prem vs cloud → set by utilization and data residency; (3) the facility envelope → power, cooling, networking; (4) support and sovereignty. Get the first two right and the rest follows.

TL;DR — the CIO checklist

• Right-size to the workload first. A 70B model serves on 1 GPU; a 405B needs a node; frontier training needs rack-scale. Don't over-buy.
• On-prem vs cloud turns on utilization (>50–60% → own) and data residency (DPDP → own).
• Budget the facility. Power, cooling, and networking often exceed the GPU cost — plan them from day one.
• Weigh sovereignty + support. India-built, India-supported hardware de-risks the supply chain and eases DPDP compliance.

This is a pillar guide

It frames the whole decision and links to the detailed spokes. Use it to sequence the choices; follow the links to size each one.

Step 1 — Right-size to the workload (the biggest lever)

Everything starts with what you're running and whether you're training or serving:

• Serving (inference). Memory decides fit: a 70B model needs ~140 GB (FP16) down to ~43 GB (4-bit) — 1 GPU; a 405B needs a node or 2× H200 quantized. → *How Much VRAM for a 70B / 405B LLM?*
• Fine-tuning. Method dominates: QLoRA fits a 70B on one GPU; full fine-tune needs ~8× H200. → *How Many GPUs to Fine-Tune a 70B LLM On-Prem?*
• Frontier training / high concurrency. When a model or its throughput exceeds one node, you move to rack-scale. → *What Is an AI Factory?*

The discipline: name the atomic unit (a workstation, an 8-GPU node, a rack) and buy in those blocks. Over-provisioning idle GPUs is the most common — and most expensive — mistake.

Step 2 — On-prem vs cloud

Two variables decide it: utilization and data residency. Own when GPUs will run >50–60% of the time over 2–3 years, or when DPDP requires data (and therefore compute) to stay in-country and under your control. Rent for bursty, short-lived, or experimental work. → *On-Prem vs Cloud GPU: True TCO for AI Training in India.*

Step 3 — The facility envelope

The GPUs are often the smaller line item. Plan for: power (dense nodes draw ~10 kW; racks far more), cooling (air below a threshold, liquid above it), and networking (NDR InfiniBand or a lossless Ethernet fabric so gradient/activation traffic doesn't starve the GPUs), plus storage fast enough to keep them fed. → *Air-Cooled vs Liquid-Cooled GPU Racks* and *Storage Architecture for AI Training.*

Step 4 — Sovereignty & support

For Indian enterprises and PSUs, data residency and supply-chain resilience are strategic, not cosmetic. India-built, India-supported hardware keeps compute and support domestic and simplifies DPDP compliance. → *Sovereign AI in India.*

Assumptions & scope

A decision framework, not a bill of materials. Prices and model sizes change quarterly — validate each spoke before purchase. Sizing figures are planning estimates.

Where RDP GPU Mart fits

RDP GPU Mart is the India-first place to execute this guide end-to-end — workstations, GPU servers, and DRACO rack-scale systems, all designed, manufactured, and supported in India, with INR-transparent pricing and DPDP-aware deployment. Start at the block your workload needs and scale up. *(Browse GPU servers or request a quote at RDP GPU Mart.)*

FAQ

How do I size AI infrastructure? Work backward from the workload: model size + training/inference set GPU count and memory; then decide on-prem vs cloud; then plan power, cooling, and networking.

When should a CIO choose on-prem over cloud? When GPUs run >50–60% sustained for 2–3 years, or when DPDP/data-residency requires in-country, in-control compute.

What's the most common buying mistake? Over-provisioning — buying rack-scale when a single node (or one GPU) meets the workload. Right-size first.

Why does India-built hardware matter for AI? It keeps compute and support domestic, de-risks the supply chain, and eases DPDP data-residency compliance.

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Related (spokes)

• On-Prem vs Cloud GPU: True TCO for AI Training in India
• How Much VRAM for a 70B / 405B LLM (Inference)?
• How Many GPUs to Fine-Tune a 70B LLM On-Prem?
• Sovereign AI in India · What Is an AI Factory?

Research log (Rule #1)

1. Spheron (2026) — GPU cloud pricing (rent-vs-buy signal). https://www.spheron.network/blog/gpu-cloud-pricing-comparison-2026/ 2. IntuitionLabs (2026) — NVIDIA GPU/system pricing. https://intuitionlabs.ai/articles/nvidia-ai-gpu-pricing-guide 3. apxml (2026) — model VRAM by size/precision. https://apxml.com/models/llama-3-1-405b 4. IndiaAI (2026) — sovereign compute context (₹65/GPU-hr). https://indiaai.gov.in/hub/indiaai-compute-capacity 5. NVIDIA — H200 datasheet / GB200 NVL72 (node vs rack). https://www.nvidia.com/en-us/data-center/h200/

For a practical next step, compare RDP GPU workstation and server options against the workload, governance, and procurement criteria in this guide.