## 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