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Built for Hyperscale. Ready for Sovereign AI.

Most data centers in India weren't designed with GPUs in mind. They were built for servers that draw 5 to 8 kilowatts a rack, then patched together with extra fans and chillers once AI workloads showed up. Cyfuture AI started from a different blueprint. The 100 MW Liquid Cooled AI Data Center is a hyperscale campus built around GPU thermal loads from day one, not retrofitted around them later. Every block runs on Direct-to-Chip liquid cooling, supports rack densities past 240kW, and sits inside an SEZ enclave designed for duty-free hardware import. Phase 1, a 15 MW block, is already live — the phases after it scale the campus toward its full 100 MW footprint, one validated block at a time.

100 MW

Total Campus Capacity

Phased Build-Out

240kW+

Max Rack Density

D2C / RDHx / Hybrid

800G

Network Fabric

InfiniBand / RoCE v2

SEZ

Duty-Free Location

Zero-Rated IGST

15 MW

Phase 1 Capacity

Live — Go-Live 31 Oct 2026

60,000+

GPUs at Full Scale

Blackwell / GB200 / Vera Rubin

Tier III

Design Redundancy

N+1 Power & Cooling

<1.3

Target PUE

Industry Avg: 1.57

GPU rig

The Physics Behind a Liquid Cooled AI Data Center

A single B200 or GB200 rack can throw off more heat in an hour than a rack of ordinary servers manages in a week. Air can't carry that much heat away fast enough — not without banks of fans burning through roughly a quarter of your power budget just moving air around. Liquid can. Water and dielectric coolants move heat somewhere in the neighborhood of 3,000 times more efficiently than air for the same volume, which is a big part of why almost every serious AI data center built after 2024 leans on some form of liquid cooling.

Here's what that gap looks like in practice, measured against a standard air-cooled hall:

Metric Air Cooling Liquid Cooling (D2C)
Max rack density 15–20 kW Up to 240 kW+
Heat capacity (volumetric) Baseline ~3,000× greater than air
Heat removal efficiency 1× 23× more effective
Fan power waste ~25% of power budget Eliminated / minimized
GPU hardware lifespan Baseline ~2× longer
PUE (Power Usage Effectiveness) 1.5–1.7 industry average <1.3 Cyfuture target

Not sure how much capacity
you actually need?

From a single rack to a dedicated MW block, our data center team will map your GPU roadmap against what's live now and what's coming online next.

Talk to Our Team
100MW-liquid-cooled-ai-data-center

How Direct-to-Chip Cooling Runs Across the Campus

Walk into any hall on this campus and the cooling story looks the same, whether you're standing in the first 15 MW block or a future one. Coolant runs from an OEM-specific manifold straight to cold plates mounted on each GPU. The fluid picks up heat at the chip, carries it through a closed loop to a Coolant Distribution Unit serving that hall, and hands it off to the facility's chillers and dry coolers before coming back around.

Nothing about that changes as capacity comes online. Every block that gets added after Phase 1 uses the same manifold design and the same CDU zoning — no redesign, no retrofit, no thermal surprises as the campus scales toward 100 MW. A campus that reinvents its cooling every 15 MW isn't really hyperscale, it's a string of one-off builds sharing a name.

The Phase 1 block is live, with go-live scheduled for 31 October 2026. Later phases add liquid-cooled IT load in MW increments as demand and construction milestones line up, scaling the same validated design toward the full 100 MW footprint.

Core AI Data Center Infrastructure Specifications

Every system on this campus — power distribution, cooling loops, network fabric — was sized around the electrical and thermal profile of modern GPU clusters. None of it was adapted from a legacy colocation design after the fact.

100MW-liquid-cooled-ai-data-center
Parameter Specification
Planned campus IT load 100 MW hyperscale build-out, deployed in phased MW blocks
Cooling architecture Direct-to-Chip (D2C) liquid loops, Rear-Door Heat Exchangers (RDHx), hybrid air/liquid zones, configurable CDUs and manifolds
Rack density range ~20 kW to 240 kW+ per rack, validated per OEM chipset
Supported chipsets NVIDIA H100/H200/B200/B300/GB200/GB300/Vera Rubin NVL72, AMD Instinct MI300X/MI350/MI450, Intel Gaudi 2 & 3, cloud ASICs, custom OEM racks
Network fabric 400G/800G fabric-ready, InfiniBand NDR/XDR, RoCEv2, non-blocking spine-leaf
Storage High-throughput NVMe, AI dataset lake, parallel file systems, object storage
Power redundancy N+1 / 2N redundancy, UPS-backed critical power, generator backup, dual-corded feeds
Monitoring & DCIM DCIM, BMS, EPMS, rack telemetry, coolant temp/pressure/flow monitoring, leak detection
Compliance MeitY empanelled, ISO 27001 controls, SEZ-enabled, 24x7 NOC/SOC
Rollout Phase 1 (15 MW) live — go-live 31 Oct 2026; later phases scale toward 100 MW

Building on GB200,
GB300, or Vera Rubin?

This infrastructure is already validated for 240kW a rack. Send us your chipset roadmap and we'll confirm fit before anything gets signed.

Check GPU Fit
H200 GPUs

Rack Density by Cooling Mode

Not every workload needs the top tier of cooling — the campus supports whichever mode actually fits the chipset in front of it.

Cooling Mode Max Rack Density Use Case
Air cooling ≤20 kW Legacy workloads — not suitable for modern AI GPUs
Rear-Door Heat Exchanger (RDHx) ~40–60 kW Mid-density AI and HPC workloads
Direct-to-Chip (D2C) ~80–100 kW High-density GPU training clusters
Ultra-HD CDU 240 kW+ validated Vera Rubin NVL72 / GB300 NVL72 / AMD Helios-class racks

Ready for Every GPU Generation

A hyperscale AI data center shouldn't need a redesign every GPU cycle. The same 240 kW/rack D2C loops and 800G fabric that support current chipsets are already sized for what's shipping next.

GPU Platform TDP per Rack Cooling Requirement Campus Readiness
Blackwell (B200/B300) Up to ~120 kW Liquid strongly recommended Fully ready
Grace Blackwell (GB200/GB300 NVL72) ~120 kW (NVL72) 100% liquid — D2C mandatory Fully ready
Vera Rubin NVL72 (H2 2026) 150–240 kW 100% liquid mandatory Infrastructure ready — 240 kW/rack D2C in place
AMD Instinct MI450/MI455X (H2 2026) ~1,500W per GPU D2C liquid mandatory Ready H2 2026

Voices of Innovation: How We're Shaping AI Together

We're not just delivering AI infrastructure-we're your trusted AI solutions provider, empowering enterprises to lead the AI revolution and build the future with breakthrough generative AI models.

KPMG optimized workflows, automating tasks and boosting efficiency across teams.

H&R Block unlocked organizational knowledge, empowering faster, more accurate client responses.

TomTom AI has introduced an AI assistant for in-car digital cockpits while simplifying its mapmaking with AI.

GPUs Validated for This Campus

Every chipset below has been checked against the campus's D2C loops, manifold sizing, and power delivery — not just listed as "supported" on a spec sheet. Tap through to a chipset's page for pricing, configurations, and availability.

GPUs Validated for This Campus

NVIDIA B200

Blackwell-generation training and inference GPU, deployed on D2C loops rated well above its rack-level heat output.

View B200 GPU Cloud
NVIDIA B200

NVIDIA B300

Higher-memory Blackwell variant for large-context inference and training, validated on the same rack and cooling design as B200.

View B300 GPU Cloud
NVIDIA GB200 NVL72

NVIDIA GB200 NVL72

Rack-scale Grace Blackwell system running on mandatory 100% liquid cooling — the density this campus's D2C loops were built for.

View GB200 GPU Cloud
NVIDIA GB300 NVL72

NVIDIA GB300 NVL72

The next step up in Grace Blackwell rack-scale density, running on the same validated manifold and CDU zoning as every other block.

View GB300 GPU Cloud
NVIDIA Vera Rubin NVL72

NVIDIA Vera Rubin NVL72

Shipping H2 2026 at 150–240 kW a rack with no air-cooled option — this campus's Ultra-HD CDU tier is already built to that ceiling.

View Vera Rubin GPU Cloud

Network Fabric for GPU-Scale Workloads

Compute is only as fast as what connects the GPUs to each other. The campus ships with 400G/800G-ready InfiniBand and Ethernet topologies built for the east-west GPU-to-GPU traffic that dominates LLM training, distributed inference, and HPC workloads.

Layer Technology Options Primary Use Case
Network fabric 400G Ethernet, 800G fabric, InfiniBand NDR/XDR, RoCEv2 GPU-to-GPU east-west traffic for LLM training
Compute cluster Bare metal GPU, managed Kubernetes, Slurm/HPC, virtual GPU Training, inference, and HPC workloads
Storage fabric High-throughput NVMe, parallel file systems, object storage Training pipelines, checkpoint storage, model serving
Security plane Private network zones, IAM, encryption at rest/transit, tenant firewalls Multi-tenant isolation for AI colocation

Deployment Models — From a Rack to the Whole Campus

From a single rack to a dedicated hyperscale block, the campus is set up to support every stage of AI infrastructure maturity — the same deployment flexibility a modern GPU cloud platform needs to onboard tenants of any size.

Rack / Cage 1–10 racks Reserved MW Block 0.5–2 MW Anchor Tenant 2–10 MW Whole-Campus Up to 100 MW
Deployment scale, from a single dedicated rack up to a full 100 MW hyperscale lease.
ModelScaleWhat's IncludedIdeal For
Rack / Cage1–10 racksDedicated racks, D2C cooling, 400G fabric, metered powerStartups, inference APIs, AI server hosting
Reserved MW Block0.5–2 MWCommitted power block, expansion rights, SLA-backed capacityEnterprises, AI SaaS, AI cluster hosting
Anchor Tenant / Build-to-Suit2–10 MWDedicated hall, custom CDU loop, private fabricGlobal AI labs, LLM builders, HPC teams
Whole-Campus / Hyperscale LeaseUp to full 100 MWExclusive campus access, SEZ enclave, sovereign isolationHyperscalers, national AI programmes, GPU cloud operators

Need dedicated halls or
a full MW block

Anchor tenants and hyperscalers get custom CDU loops, private fabric, and expansion rights written into the term sheet from day one.

Discuss Anchor Tenancy
H200 GPUs

Purpose-Built for Every AI-First Organization

Segment Infrastructure Fit
Global AI labs & model builders Dedicated halls for multi-node GPU cluster training and RLHF, InfiniBand NDR fabric, D2C cooling validated for B200/B300/GB300 and Vera Rubin
Enterprise & BFSI Sovereign AI infrastructure for private RAG, risk models, and compliant inferencing within SEZ boundaries
AI-native SaaS & GPU cloud providers Enterprise GPU cloud capacity for inference APIs, agent platforms, and embedding pipelines on a 400G low-latency fabric
Government & public sector MeitY-empanelled sovereign AI zones with full India data residency and ISO 27001 controls
Hyperscalers & cloud OEMs Wholesale MW capacity blocks and chip validation environments for global cloud providers entering India's AI infrastructure market

SEZ Location — The CAPEX Advantage on GPU Hardware

Select blocks of the campus sit inside a Special Economic Zone, which matters more than it might sound for anyone importing GPU hardware at scale. Duty-free import on authorized equipment, zero-rated IGST on SEZ supplies, and no import licence requirement — subject to SEZ approvals and applicable law. We'd recommend bringing in your own legal, tax, and SEZ consultant before structuring anything commercial around this, since the exact benefit depends on your entity structure and equipment mix.

Security Domain Controls
Physical security Multi-layer perimeter, biometric access, 24x7 CCTV, visitor management per tenant boundary
Cybersecurity Network segmentation, tenant firewalls, IAM, continuous vulnerability management
Data protection Encryption at rest and in transit, key management, secure backup and deletion workflows
Compliance ISO 27001-aligned controls, MeitY empanelment, data residency documentation
24x7 NOC/SOC Round-the-clock monitoring, change management, incident response, capacity planning

From Reservation to Go-Live

Reserving capacity here isn't a one-click checkout — and it shouldn't be, given what's at stake. Here's how it actually goes:

Step Stage Activities
1 Workload discovery GPU/accelerator BoM, TDP profile, network and compliance requirements, NDA execution
2 Technical fit-out design Rack drawings, power path design, CDU zone sizing, network topology
3 Commercial term sheet MW/rack reservation, pricing model, SLA parameters, SEZ structure review
4 Validation & commissioning Per-rack thermal validation, FAT/SAT acceptance testing
5 Production operations 24x7 NOC/SOC, DCIM reporting, ongoing capacity planning

Ready to lock in
early-phase pricing?

Rack allocation and pricing tighten as each block fills. Reserve capacity now, and every phase after this one inherits the same validated build you're reserving into today.

Reserve Capacity
H200 GPUs

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FAQs: 100 MW Liquid Cooled AI Data Center

The power of AI, backed by human support

At Cyfuture AI, we combine advanced technology with genuine care. Our expert team is always ready to guide you through setup, resolve your queries, and ensure your experience with Cyfuture AI remains seamless. Reach out through our live chat or drop us an email at [email protected] - help is only a click away.

It's Cyfuture AI's hyperscale AI infrastructure campus in India, built in phased MW blocks on Direct-to-Chip liquid cooling. The first 15 MW block is live, and later phases scale the campus toward a full 100 MW footprint.

No — capacity comes online in phases. The 15 MW Phase 1 block is live infrastructure with anchor tenant reservations open now. Later phases add liquid-cooled IT load in MW increments as demand and construction milestones are met.

Every block here is purpose-built for AI workloads rather than retrofitted from legacy colocation — Direct-to-Chip cooling validated up to 240 kW per rack, pre-installed 800G network fabric, and SEZ-enabled locations for duty-free GPU hardware import.

NVIDIA H100, H200, B200, B300, GB200, GB300, and Vera Rubin NVL72; AMD Instinct MI300X, MI350, and MI450; Intel Gaudi 2 and 3; plus cloud ASICs and custom OEM racks — validated for both AI training clusters and HPC workloads.

Rack density ranges from roughly 20 kW on RDHx configurations up to 240 kW+ on the Ultra-HD CDU tier, validated per OEM chipset — well beyond the ~15–20 kW ceiling of air-cooled infrastructure.

Four models that scale with tenant maturity: Rack/Cage for AI server hosting, Reserved MW Block for AI cluster hosting, Anchor Tenant/Build-to-Suit for dedicated halls, and Whole-Campus Lease for hyperscalers and sovereign AI programmes.

Yes. Vera Rubin NVL72 mandates 100% liquid cooling, with no air-cooled configuration available. The campus's 240 kW/rack D2C infrastructure is designed to support this density from day one of each phase.

Select campus blocks sit within a Special Economic Zone, enabling duty-free import of GPU hardware and infrastructure equipment, zero-rated IGST on SEZ supplies, and simplified customs handling — subject to SEZ approvals and applicable law.

Reservations start with a technical briefing and workload discovery call — no upfront commitment required. From there, the engagement moves through fit-out design, a commercial term sheet, thermal validation, and commissioning ahead of go-live.

Lease Entire 100 MW Entire Campus

Secure Direct-to-Chip liquid cooling capacity now — pricing and rack allocation lock in early as the 100 MW campus scales.

Reserve Your Block

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