Why AI Networking is different ?

Design, Deploy and Operate your Private AI Infrastructure

AI Storage

Traditional enterprise networks were built for users reaching applications. AI clusters do the opposite — hundreds of GPUs talk to each other, continuously, at line rate. The same switch fabric cannot serve both jobs well.

‍Traditional Enterprise

North-South Traffic

Users access centralized applications, databases, and internet resources through hierarchical networks. Traffic is bursty, asynchronous, and tolerant of moderate latency and jitter.

Traffic Direction

Routing scale & segmentation

WAN connectivity & SD-WAN

Internet edge performance

Mixed application environments

AI Cluster Fabric

East-West GPU Traffic

The majority of traffic stays inside the cluster: GPU-to-GPU tensor exchange, parameter sync, gradient all-reduce, checkpoint streaming. Continuous, synchronized, microsecond-sensitive.

Traffic Direction

Ultra-low latency & jitter

Massive bisection bandwidth

RDMA & GPUDirect

Lossless, congestion-managed

Engineered for the way
AI actually moves data

RDMA, lossless transport, adaptive routing, in-network reduction

Capabilities

Every feature exists for a reason — to keep your GPUs fed, your gradients moving, and your training jobs scaling linearly across the cluster.

RDMA & GPU Direct

Zero-copy transfers straight from GPU memory to the wire. Bypass the CPU, bypass the kernel, keep tensors moving.

SHARP In-Network Reduction

Collective operations like all-reduce run inside the switch fabric. Cut step time, free up GPU cycles.

Adaptive Routing

The fabric reroutes around congestion in microseconds. No more hot spines tanking your scaling efficiency.

Lossless Transport

PFC and ECN tuned per-fabric. No packet drops, no NCCL timeouts, no retransmission storms killing throughput.

Multi-Tenant Isolation

EVPN/VxLAN with per-tenant QoS. Many AI customers, one fabric, zero interference between training jobs.

Telemetry & Observability

Real-time link, queue, and NCCL signal monitoring. Watch fabric health alongside GPU utilization, not after it.

Three networks. One cluster.

GPU-to-GPU fabric, storage fabric, and out-of-band management

Architecture

A production AI cluster is not one network — it is three fabrics, each tuned for a different traffic profile. Designed independently, deployed as a single coherent system.

East-West · Horizontal

GPU-to-GPU Fabric

Distributed AI training requires GPUs across multiple servers to continuously exchange tensors, gradients, activations, and model states. NVIDIA InfiniBand dominates hyperscale AI training because it provides RDMA, GPUDirect RDMA, adaptive routing, congestion control, SHARP in-network reduction, and highly deterministic, ultra-low latency performance.

infiniBand NDR/XDR or RoCEv2 over Ethernet
Non-blocking leaf-spine, 1:1 oversubscription
GPUDirect RDMA, SHARP, adaptive routing
Tuned for all-reduce, all-gather, reduce-scatter

800G

Per-port BW

<1µs

Switch latency

95%+

Scaling eff.

North-South · Storage

Storage-to-GPU Fabric

One of the most critical fabrics in the cluster. Modern AI workloads continuously move massive amounts of data between GPUs and storage during training, checkpointing, and inference. A poorly designed storage fabric creates bottlenecks that leave expensive GPUs idle waiting for data.

Dataset ingestion at line rate
High-throughput checkpoint storage
Parallel file system access (Lustre, GPFS, WekaFS)
RDMA-enabled data paths end-to-end

400G

Per-port BW

18+9

Fast/Long-term

RDMA

End-to-end

Management · Independent

OOB Management Network

A dedicated management network that operates independently from the high-speed AI data fabric. Used for server provisioning, BMC/IPMI access, monitoring, firmware management, remote troubleshooting, PXE boot, and infrastructure automation — preserving operator control even during production fabric incidents.

BMC / IPMI / Redfish access
PXE boot & firmware lifecycle
Telemetry & monitoring out-of-path
Hardened OOB firewall, separate from data plane

25G

Server links

10G

Mgmt links

100%

Isolated

Built for the scale your training run needs.

Numbers from production fabrics we've designed, deployed, and operated

Reference Spec

A representative 64-node H200 + B300 deployment on OneSourceCloud's reference architecture — non-blocking, lossless, RDMA end-to-end.

800G

InfiniBand NDR per GPU port

B300 / Blackwell-class

<1µs

Switch port-to-port latency

Deterministic, lossless

95%

Distributed training scaling efficiency

128-GPU NCCL all-reduce

1:1

Bisection bandwidth, non-blocking

Leaf-spine, no oversubscription

2,304

GPU ports at one fabric domain

B300 × 16 + H200 × 48

0

Packet drops under target load

PFC + ECN tuned

24/7

Fabric NOC & telemetry

RDMA-aware monitoring

N+1

Spine resilience & failover

UFM-managed

Design. Deploy. Operate.

A complete lifecycle for the AI fabric, from architecture to 24×7 NOC

End-to-End Service

Engineered to your workload, built into your data center, and continuously tuned as the cluster scales and the workload mix shifts. One team owns the fabric.

Architecture

Network Design

A non-blocking fabric engineered around your workload profile, GPU density, and growth plan. Bandwidth, latency, and oversubscription analyzed before a single port is racked.

InfiniBand or RoCE fabric architecture

Leaf-spine / Clos topology & bisection planning

RDMA & GPUDirect architecture

Storage fabric & OOB management design

Congestion, QoS, and adaptive routing strategy

Multi-tenant segmentation & HA planning

Build

Network Deployment

Switch installation, structured cabling, configuration, integration, validation. The architecture becomes a production fabric tuned for GPU traffic, storage access, and distributed training.

Switch & spine-leaf interconnection

Fiber, transceiver, structured cabling

L2/L3, VLAN, EVPN/VxLAN config

RDMA, PFC, ECN, QoS tuning

NIC, PCIe locality, storage integration

NCCL, latency, bandwidth, failover validation

Operate

Network Management

Continuous oversight, monitoring, and tuning. AI workloads are sensitive to congestion, latency, and packet loss — proactive management is what keeps GPU utilization at the line you paid for.

24×7 fabric monitoring & NOC

RDMA / NCCL performance analytics

Packet loss, latency, queue-depth analysis

Firmware lifecycle & change management

Incident response & root cause analysis

Capacity planning & expansion support

Network

Frequently asked questions

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