AI Data Centers in India: What TCS’ HyperVault Means for Compute, Resources, and Growth

Tata Consultancy Services (TCS) and private equity partner TPG have launched HyperVault, a multi-year initiative to build gigawatt-scale AI data centers in India. Backed by an initial $1 billion commitment within a planned $2 billion program, HyperVault targets the rising demand for AI compute by developing high-density facilities designed specifically for modern GPU workloads.

Why gigawatt-scale AI data centers are critical now

AI models and applications are driving unprecedented growth in compute demand. India’s rapidly expanding digital footprint—generating a large share of global data—has created pressure on local capacity for AI workloads. HyperVault responds to a market opportunity: enterprises, cloud providers, and AI companies require local, performant infrastructure to run training and inference while meeting latency, compliance, and sovereignty requirements.

High-density design for AI compute

HyperVault emphasizes liquid-cooled, high-density racks and networking designed for dense GPU clusters. These architectures enable far greater power efficiency and compute density than legacy CPU-focused data centers, but they also introduce new operational requirements:

• Specialized cooling systems (direct-to-chip liquid cooling, immersion cooling)
• Upgraded power distribution and redundancy to support megawatt-plus pods
• Low-latency network fabrics to interconnect distributed GPU cabinets and link to major cloud regions
• Designs optimized for modular expansion to scale from megawatts to gigawatts

What are the environmental and resource impacts of gigawatt-scale AI data centers in India?

This question highlights the most actionable concerns and is optimized for quick answers: gigawatt-scale AI data centers can significantly increase water and electricity demand, and require sizeable land parcels in or near urban hubs—creating stress on local resources if not planned carefully.

Water use

Liquid cooling and evaporative systems can raise data center water consumption compared with air-cooled installations. In regions facing seasonal shortages, such as parts of Mumbai, Bengaluru, and Chennai, water-intensive cooling strategies may conflict with municipal needs and agriculture.

Power supply

High-density AI clusters demand reliable, high-capacity electrical feeds. Securing grid capacity, substations, and stable supply with minimal outage risk is a major bottleneck in dense urban centers. This amplifies the need for utility-grade agreements, onsite generation, and long-term energy procurement strategies.

Land and urban planning

Gigawatt-scale deployments require large industrial parcels and careful zoning to avoid conflicts with residential and ecological areas. Urban land scarcity can push projects to peripheral regions, adding latency and transmission complexity.

How HyperVault fits into India’s broader data-center expansion

Industry estimates project a dramatic rise in national capacity over the coming years. HyperVault’s initial phase targets roughly 1.2 gigawatts of installed capacity—part of a broader trend toward both leased facilities and hyperscaler-owned AI campuses. This expansion complements earlier investments and announcements from cloud providers and AI firms that are scaling local footprints across the country.

For additional context on national infrastructure risks and growth dynamics, see our analysis on Is an AI Infrastructure Bubble Brewing? Data Center Risks and a detailed look at how compute facilities reshape power demand in Data Center Energy Demand: How AI Centers Reshape Power Use.

Operational and technical best practices to reduce impact

Designing and operating gigawatt-scale AI data centers in resource-constrained environments requires a layered approach. Successful projects typically combine:

1. Cooling innovation and water stewardship
2. Renewable energy procurement and onsite generation
3. Flexible land-use planning and modular deployment
4. Partnerships with local utilities and government for long-term capacity planning

Cooling and water strategies

To minimize fresh water use, operators can choose low-water cooling options and water-reuse systems:

• Adopt immersion cooling where feasible to reduce reliance on evaporative cooling
• Implement closed-loop liquid cooling with onsite heat recovery and reuse
• Deploy hybrid air/liquid systems that lower water intensity during drought conditions
• Invest in gray-water capture, treatment, and reuse programs

Energy sourcing and efficiency

Power is both a cost and a reliability challenge. Leading practices include:

• Long-term renewable power purchase agreements (PPAs) to lock in green energy and stabilize costs
• Onsite solar, paired with battery energy storage systems (BESS) for outages and peak shaving
• Microgrids that integrate local generation, storage, and demand response
• Server-level efficiency improvements such as advanced power management and optimized rack-level airflow

Investments in chip- and system-level energy efficiency can multiply the benefit. For instance, progress on power-efficient hardware and chiplet architectures reduces total facility-level power needs—an important lever discussed in our piece on Power-Efficient Chiplets: Cutting AI Chip Power by 50%.

What are the regulatory and policy actions needed?

To ensure sustainable scaling, coordinated policy and regulatory frameworks are critical. Governments, regulators, and industry should consider:

• Clear land-use and zoning pathways for hyperscale and modular AI campuses
• Water allocation rules that prioritize critical community needs and incentivize reuse
• Grid upgrade plans and expedited permitting for substations to support reliable high-capacity connections
• Incentives for renewable PPAs, energy storage, and waste-heat recovery to align infrastructure with decarbonization goals

Commercial implications for hyperscalers, enterprises, and local providers

HyperVault represents a business model combining private capital, local expertise, and enterprise demand. Its commercial impacts include:

• Faster onshore capacity for AI workloads, reducing latency and improving compliance for customers
• An expanded market for colocation and managed AI infrastructure services
• Increased demand for specialized data-center engineering, cooling, and power systems from local suppliers
• Opportunities for enterprises to colocate sensitive workloads near large AI clusters

Most new capacity in the next several years is expected to be delivered via leased facilities, creating opportunities for local operators and system integrators to partner with hyperscalers and AI firms on deployment, operations, and maintenance.

How can operators balance growth with sustainability?

Balancing rapid expansion and environmental stewardship requires an integrated strategy:

1. Design for efficiency first: optimize rack density and cooling at the outset.
2. Choose site locations based on grid resilience, land availability, and water risk assessments.
3. Embed circular-water solutions and reuse systems into the facility lifecycle plan.
4. Engage with local stakeholders and regulators early to align infrastructure plans with community needs.
5. Commit to measurable sustainability goals and transparent reporting on energy, water, and emissions.

Timeline, scale, and market forecast

HyperVault’s first phase targets around 1.2 gigawatts of capacity. Industry projections suggest national capacity could grow multiple-fold by the end of the decade as cloud providers, hyperscalers, and independent operators build out infrastructure. That growth will reshape supply chains, land markets, and energy systems—making careful planning a necessity rather than an option.

Risks and contingency planning

Key risks that operators must plan for include supply-chain delays for specialized cooling and power equipment, grid curtailment or outages during peak demand, water shortages during drought cycles, and community or regulatory objections tied to resource allocation. Contingency measures include diversified supply contracts, hybrid cooling fallbacks, onsite energy reserves, and phased buildouts that decouple compute scale from immediate resource strain.

Checklist for responsible gigawatt-scale deployments

• Complete a comprehensive water-stress and grid-resilience assessment
• Model lifecycle carbon and water impacts for design choices
• Secure long-term energy agreements that favor renewables
• Integrate modular build paths to manage pace and community impact
• Publish transparent sustainability targets and progress

What this means for India’s AI future

HyperVault and similar initiatives will accelerate local AI compute availability, enabling faster model development and delivery across industries—from healthcare and education to finance and agriculture. When deployed responsibly, gigawatt-scale AI data centers can power innovation while driving local investment and job creation in engineering, construction, and operations. But the trade-offs around water, power, and land require deliberate technology choices and policy frameworks to ensure that infrastructure growth is sustainable and equitable.

Next steps for stakeholders

For policymakers: establish clear permitting pathways, incentivize renewable PPAs, and mandate water reuse standards for high-intensity facilities. For operators: adopt low-water cooling, secure diversified energy sources, and engage communities early. For enterprises and hyperscalers: prioritize partnerships with operators that demonstrate measurable sustainability commitments and technical readiness for AI workloads.

Conclusion and call to action

TCS’ HyperVault signals a new phase in India’s AI infrastructure buildout: rapid, capital-intensive, and technically sophisticated. The program can close a critical gap in local compute supply—but only if developers, regulators, and operators align on resource stewardship, grid planning, and sustainable design.

If you manage infrastructure, policy, or AI operations, now is the time to act: evaluate your cooling and power strategies, engage with potential partners, and prioritize designs that balance performance with sustainability. Stay informed and contribute to shaping responsible AI infrastructure in India.

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