Solutions / AI Training

Frontier-scale
training, in India.

Multi-gigawatt AI training capacity at the rack-density, power architecture, and cooling design the Vera Rubin Ultra generation requires. 600 kW per rack. 800VDC distribution. Direct-to-chip liquid cooling. Designed for sustained training runs, not for the workload assumptions of the previous decade.

The Problem

The training stack of 2027
is not the stack of 2024.

Rack-scale AI platforms have changed the operating point of data center design. NVIDIA Vera Rubin Ultra (NVL576) is specified at 600 kW per rack at production. AMD Helios MI450 follows close behind. Most existing Indian capacity was designed for a 5-20 kW rack envelope.

The gap is structural, not incremental. A facility designed for 10 kW per rack cannot host a 600 kW rack by adding capacity. The power distribution architecture is wrong. The cooling architecture is wrong. The mechanical and electrical infrastructure is wrong. Retrofitting is, in practice, demolition and rebuild.

For an AI lab planning a multi-billion-parameter training run in 2027 or 2028, the choice of training infrastructure is the choice of architecture: 800VDC or 415VAC; direct-to-chip liquid or evaporative air; AI-native or legacy retrofit; large unified campus or distributed micro-facilities.

HyperNext made these architectural choices at design time. The Kakinada AI Factory is 1.2 GW IT load, AI-native from foundation slab. The Hyderabad facility opens in December 2026 with the same architecture at smaller initial scale.

The HyperNext Answer

Architecture, not
a wrapped service.

Four design choices, made at the architectural level, that distinguish this solution from a re-packaged commodity hosting offer.

TRAIN / 01

600 kW per rack, by design

Rack envelopes specified for sustained 600 kW operation, matching the Vera Rubin Ultra NVL576 production specification. Mechanical and electrical infrastructure sized to support the rack density at the campus level. Design basis documented in HN-RP-002.

TRAIN / 02

800VDC power architecture

Higher-voltage DC distribution reduces conversion losses, lowers PUE, and improves resilience at AI rack density. Sourced from 700 MW of captive solar at Khavda plus partnership wind. The arithmetic is in HN-RP-002.

TRAIN / 03

Direct-to-chip liquid cooling

Coolant-distribution architecture designed for the heat reject loads of NVL576-class systems. Closed-loop glycol coolant primary, sealed and recirculated. Heat rejected by dry coolers only, with no evaporative cooling. Cooling architecture in HN-RP-006.

TRAIN / 04

Multi-gigawatt campus delivery

1.2 GW IT load at Kakinada AI Factory (Q1 2028 first phase); 250 MW Hyderabad (initial 64 MW Dec 2026); 100 MW Nava Raipur DRaaS site. Anchor tenant capacity available now for 2027-2028 commissioning windows.

Reference Deployment

A 5,000-GPU training
cluster, end to end.

A worked example for a frontier AI lab training a multi-billion-parameter model in 2027.

Cluster size
5,000 GPUs as NVIDIA Vera Rubin Ultra NVL576; approximately 70 racks at 600 kW; 42 MW IT load.
Site
Kakinada AI Factory, first phase. Direct interconnect to international subsea cable landings via Chennai.
Power
800VDC distribution. Captive renewable energy from Khavda solar plus wind. PUE design target 1.25 to 1.35.
Cooling
Direct-to-chip liquid cooling on the rack-scale platform. Closed-loop glycol coolant. Dry coolers only, no evaporative supplement.
Storage
Multi-petabyte high-throughput parallel filesystem; tiered hot/warm storage; checkpointing optimised for sustained training run.
Network
NVIDIA NVLink Switch System fabric within rack; 800Gbps InfiniBand across racks; dedicated 400Gbps egress for dataset and checkpoint transfer.
Sustainability
85% renewable energy fraction at commissioning. Nagmati watershed credit applied to workload water footprint. Quarterly ISO/IEC 30134-9 reporting.
Commercial
Multi-year capacity reservation with anchor-tenant terms. Take-or-pay structure. Service level commitments on uptime and rack availability.
Related Research

The methodology
behind the solution.

The architectural choices on this page are documented in the HyperNext Research series. Methodology is published openly so that customers can verify the engineering claims and so that other operators can run the same analysis on their own facilities.

Discuss Your Requirements

Talk to HyperNext.

A 30-minute conversation with our business development team, oriented to your specific workload, regulatory requirements, and deployment timeline. No pricing reveals, no over-promised SLAs. Just a working conversation about whether HyperNext is the right fit.