AI INFRASTRUCTURE NETWORKING

The network behind AI performance.

Design GPU fabrics for demanding east-west traffic, storage access and observable congestion.

ENGINEERING PERSPECTIVE

Architecture that stands up in practice.

We distinguish scale-out compute, storage and management networks. Fabric design accounts for workload communication patterns, oversubscription and collective operations. Where RoCE is selected, ECN, PFC and buffer behavior need end-to-end validation rather than generic settings.

Serving organizations across India, including Punjab, Ludhiana, Mohali and Chandigarh.

WHERE IT FITS

Multi-rack GPU clusters, distributed training and high-throughput inference platforms.

Leaf-spine fabrics

High-speed Ethernet

GPU east-west traffic

RoCE considerations

Congestion management

Fabric observability

REFERENCE ARCHITECTURECOMPUTE + FABRIC

GPU compute

Storage

Leaf fabricCapacity · congestion · resilience

Spine layer

Peer leafs

Management

Conceptual leaf-spine connectivity. Compute, storage and management paths require workload-specific design.

WHAT THE ENGAGEMENT PRODUCES

Decisions made clear.
Delivery made practical.

Fabric topology and capacity model

Congestion and QoS design

Throughput, latency and failure tests

HOW WE ENGAGE

From assessment
to operational handover.

We agree scope and acceptance criteria, validate the design in a pilot, control the implementation and document the environment for the people who run it.