High-density rack power
Built to support high-density power loads per cabinet, with distribution and metering designed for accelerated compute rather than retrofitted onto a general-purpose floor.
The shift
The data centre is becoming the AI factory.
Artificial Intelligence and Machine Learning applications demand a complete rethink of data centre capacity, power density and thermal management. A cabinet designed for general enterprise compute cannot absorb an accelerated node without hitting a power or thermal ceiling first.
Webyne approaches AI capacity as a design problem rather than an upgrade — sizing power, cooling, interconnect and storage together so a cluster performs at the density it was specified for.
The AI infrastructure stack
Seven layers, engineered together.
Each layer constrains the next. Designing them in isolation is what causes stranded capacity.
Power
Cooling
Compute
Storage
Network
Cloud
Security
Core capabilities
What makes a rack AI-ready.
Liquid & advanced cooling
Precision cooling designed for high-TDP GPU nodes, including liquid-assisted options where air alone cannot carry the thermal load.
Low-latency fabric
High-speed internal interconnects for distributed AI training models, keeping east-west traffic deterministic across the cluster.
High-throughput storage
NVMe-backed storage tiers sized to keep accelerators fed rather than waiting on I/O.
Scalable deployment models
Capacity models that expand from a pilot cluster to large data-model deployments without redesign.
Isolation & security
Single-tenant zones with audited physical and logical access for sensitive model and dataset workloads.
Workload profiles
Training and inference have different appetites.
We size deployments against the workload profile rather than a single generic GPU tier.
- Distributed training Sustained high power draw, dense interconnect, large checkpoint I/O.
- Inference at scale Latency-sensitive, wide horizontal scaling, tighter availability requirements.
- Fine-tuning & research Bursty utilisation with flexible capacity and shorter commitment cycles.
- Data preparation Storage- and network-bound pipelines feeding the accelerator tier.
Priority domains
Where deep research meets real-world demand.
Artificial Intelligence
Machine Learning
HPC
Edge AI
Industrial AI
Computer Vision
Applied Research
Data Science
Common questions
What infrastructure teams ask us first.
Straight answers on density, cooling and commercial models.
What rack density can you support?
Density is agreed per deployment against the cooling method and power topology available in your zone. High-density cabinets are provisioned in designated areas rather than spread across a general-purpose floor, which is what keeps the thermal envelope predictable.
Do you support liquid cooling?
Yes. Liquid-assisted cooling is deployed where high-TDP nodes make air cooling impractical. The right approach depends on your hardware generation and expected sustained utilisation, so we assess this during design.
Can we start small and scale?
Yes. Deployment models are designed to expand from a pilot cluster to a full production footprint without re-architecting power, cooling or interconnect.
Is the network suitable for distributed training?
The fabric is designed for low-latency east-west traffic, which is the dominant pattern in distributed training. Interconnect topology is agreed as part of cluster design.
Inside an AI cluster
Power, cooling and fabric, sized together.
Accelerated nodes draw more and reject more heat than general enterprise compute. Designing the three together is what prevents stranded capacity.
- High-density rack power Distribution and metering built for accelerated compute.
- Liquid-assisted cooling Deployed where air alone cannot carry the thermal load.
- Low-latency fabric Deterministic east-west paths for distributed training.
Bring us your cluster requirements.
Share your accelerator type, node count and utilisation profile — we will come back with a density and cooling plan.
Sales: +91 98765 43210
Email: info@webynedc.com