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## INDUSTRY OUTLOOK

Capacity trajectory:

* 2007: 75 MW → 

2020: 597 MW → 

2025: 1,650 MW → 2030E: 5,000 MW

* 26% CAGR over next 5 years

* 3 GW active pipeline | ~$25Bn capex needed

The S-curve story:

2G/3G (2007–14) → smartphones + Jio 4G (2016–20) → COVID digital shift → 5G + cloud (2022–24) → AI inflection (2025+)

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## 🎯 TRENDS

Dual demand engine:

1️⃣ Foundational: cloud adoption, data localization, 5G, BCP

2️⃣ AI workloads: IndiaAI Mission, enterprise AI, sovereign compute

Structural shifts underway:

* MW → GW scale competition (AI factories)

* Training → Inference dominance (global GPU spend: 34% inference in 2023 → 36% in 2027, growing 5x faster)

* Air-cooled → Liquid- cooled standard

* Colocation → Vertical integration (DC + GPU cloud + software)

* Domestic-only → Global capital + global operators entering

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## AI ADOPTION INSIGHTS

* India = 2nd largest ChatGPT user base globally (9% share, behind US at 18%)

* AI market: $13Bn (2025) → $130Bn (2032) at 39% CAGR

* 45% of enterprises already deploying AI; only 6% haven’t started

* 64% want in-house AI on cloud GPUs — the demand bedrock for domestic Neoclouds

* 1,800+ GCCs (500+ AI-focused); 89% of new startups AI-native

Segment-wise AI market (2025, $13Bn):

BFSI $2.5b | 

Startups $1.8b | 

Media $1.6b | 

Mfg $1.4b | 

Tech Svcs $1.3b | 

Public $1.2b | 

Others $3.3b

IndiaAI Mission ($125Bn / 5 yrs):

* 38,000+ GPUs committed | 22,000 (~58%) allocated

* 3,000 datasets, 243 AI models across 20 sectors

* Selected LLM developers: Sarvam, Gnani, Soket, Gan.AI

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## 🧱 KEY BUILDING BLOCKS (Value Chain)

Bottom-up stack:

1. Data Centres (Sify, NTT, Equinix) — physical infra

2. GPU Hardware (Nvidia 90–95% share, AMD ~5%, Intel <1%)

3. AI Cloud Service Providers (AWS, CoreWeave, Yotta) — the gateway

4. Compute Software (GPT, Gemini, Sarvam, DeepMind)

5. End-user Apps (Copilot, ChatGPT, Gemini)

GPU roadmap (Nvidia): 

Hopper ‘24 → 

Blackwell ‘25 → 

GB300 ‘26 → 

Rubin ‘27 → 

Feynman ‘28

Useful life logic:

* Latest gen → Training (12–15 months per LLM)

* N-1/N-2 gen → Inference (perpetual)

* 6-year financial life; longer physical life

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## 💰 GPU CLOUD UNIT ECONOMICS

Per Nvidia H200 8-GPU server:

| Metric | Value |

| Capex | ₹27.3 M |

| Revenue | ₹12.5 M/yr |

| EBITDA | ₹9.8 M/yr |

| EBITDA margin | 78.6% |

| Payback | ~2.8 yrs |

| Pricing | ₹195/GPU/hr (blended) |

| Utilization | 88% blended |

| PUE | 1.4 (liquid- cooled) |

Sized cluster (3,000 GPUs / 375 servers):

* Project IRR: 20.3% | Equity IRR: 28.4% (HTM basis)

* Leverage: 60% | Debt cost: 10% | Tenor: 5 yrs | DSCR: 1.5x

* Contract mix: 75% take-or-pay / 20% merchant / 5% spot

Pricing tiers: Take-or-pay ₹300/mo | Merchant ₹225 | Spot ₹300 (with low utilization)

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## 🗺️ DC HUB MAP (1,650 MW total)

| Hub | 

Capacity | 

Vacancy | 

U/C | 

Pipeline 

| Mumbai | 

801 MW 

2.9% 

448 MW 

893 MW

| Chennai |

268 MW | 

12.4% | 

153 | 

273 |

| Delhi-NCR | 

161 | 

10.2% | 

47 | 

270 |

| Hyderabad | 

138 | 

9.7% | 

106 | 

200 |

| Bengaluru | 

119 | 6.9% | 19 | 107 |

| Pune | 111 | 2.0% | 30 | 160 |

| Kolkata | 17 | 3.5% | 15 | 84 |

Takeaways:

* Mumbai = 50% of capacity, 47% of incremental supply (cable landing moat: 12 stations)

* Chennai = 15% incremental, 3 new subsea cables landing 2026–27

* Hyderabad = 11% incremental, hyperscaler self-build hub

* Mumbai now ranks 6th globally in under- construction capacity

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