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Networking for AI Infrastructure

Engineered for High-Throughput, Low-Latency Compute

In AI environments, networking is not just connectivity, it is a performance multiplier.

From GPU-to-GPU communication to multi-node cluster scaling, network architecture directly impacts training time, inference performance, and overall infrastructure efficiency.

DiGiCOR designs AI networking architectures built for scale, bandwidth, and reliability.

Design AI Network Architecture

Why AI Networking Is Different

Traditional Enterprise Networking

Optimised for standard workloads

  • User access
  • Application traffic
  • General data movement

AI Networking

Purpose-built for AI workloads

  • Massive east-west traffic
  • Distributed GPU communication
  • Large dataset transfers
  • High concurrency workloads
  • Ultra-low latency environments

Critical Performance Factor

As GPU counts increase, network bottlenecks can significantly reduce cluster efficiency.

⚡

Bandwidth & Latency Considerations

Performance Beyond Port Speed

AI workloads depend on both bandwidth and latency.

Key considerations include:

  • High-throughput links for dataset movement
  • Low-latency interconnects for distributed training
  • Consistent performance under sustained load
  • Balanced oversubscription ratios

Training Time Impact

In multi-node training environments, slow interconnects can dramatically increase training time.

We architect networking to keep GPUs fully utilised.

Single Node vs Multi-Node Networking

Planning for Growth

Single-Node AI Systems

  • Internal GPU interconnects
  • High-speed storage access
  • Optimised PCIe architecture

External networking may be minimal — but future scalability should be considered.

Multi-Node AI Clusters

  • High-speed Ethernet or specialised fabrics
  • Low-latency switching
  • Non-blocking architecture
  • Scalable spine-leaf topologies

Cluster networking must scale predictably as compute nodes increase.

We design for both current deployment and future expansion.

High-Speed Ethernet & Fabric Design

Choosing the Right Interconnect Strategy

AI environments commonly require:

  • 25GbE, 100GbE, or higher throughput
  • Redundant switching layers
  • Intelligent traffic segmentation
  • Support for distributed AI frameworks

The right fabric design depends on:

  • GPU density per node
  • Dataset size
  • Training parallelism
  • Cluster size
  • Budget constraints

We evaluate performance targets before recommending architecture.

Storage & Network Integration

AI storage and networking must be aligned.

High-speed storage tiers require:

  • Matching network throughput
  • Low-latency switching
  • Proper NIC selection
  • Balanced uplink capacity

Critical Insight

A high-performance storage array cannot deliver results if the network becomes the choke point.

We design end-to-end data paths — not isolated components.

Scalability & Topology Planning

Designing for Linear Performance Growth

As organisations scale AI workloads, network architecture must support:

  • Additional GPU nodes
  • Increased dataset sizes
  • Multi-team concurrency
  • Hybrid cloud integration

We design scalable topologies such as:

Spine-leaf architectures

Non-blocking, high-bandwidth fabric design

Segmented AI network zones

Isolated resources for different workload types

High-availability switching configurations

Redundancy without performance degradation

The goal is predictable performance growth — not diminishing returns.

Reliability & Enterprise Integration

AI networking must align with enterprise standards.

We incorporate:

  • Redundant paths
  • High-availability switching
  • Network segmentation
  • Security policies
  • Monitoring and visibility tools

Enterprise Ready

AI infrastructure should meet enterprise uptime and governance requirements.

Next-Generation Networking for AI Workloads

From GPU Fabrics to Intelligent Wireless

Align your cluster size, latency requirements, and budget with the right mix of AI-ready networking — from ultra-low-latency GPU fabrics to intelligent wireless driven by Mist AI.

Fabric / Platform Primary Role in AI Best-Fit Use Cases Speeds / Protocols Scale & HA Notes
InfiniBand (HDR/NDR/XDR) Lowest-latency, lossless fabric for distributed AI training. Large LLM clusters, deterministic all-reduce, synchronous jobs. 200/400/800 G (gen-dependent), native RDMA, NCCL-optimised collectives. Proven at multi-rack scale; fabric partitioning; redundant spines for HA. Best choice for ultra-large synchronous training.
Ethernet + RoCE (AI-tuned) Open, cost-efficient scale-out using RDMA over Ethernet. Mid-scale training, large-scale inference, multi-tenant AI clouds. 400/800 G Ethernet; RoCEv2; PFC/ECN; adaptive routing and QoS. Leaf-spine redundancy; telemetry-driven tuning; scalable to many racks. Excellent TCO when engineered with strict QoS and flow control.
NVIDIA Spectrum-X Ethernet AI-optimised Ethernet approaching IB-like behaviour at scale. Hyperscale AI fabrics and dense multi-tenant environments. 800 G-class ports; enhanced RoCE; SuperNIC offload and telemetry. Validated end-to-end designs; predictable latency and throughput. Ideal for Ethernet-standardised AI data centres.
NVLink + NVLink Switch Ultra-high-bandwidth in-rack GPU-to-GPU scale-up fabric. Single-rack "one big GPU" domains, pipeline/tensor parallelism within a rack. GPU-domain links (gen-dependent); complements inter-rack networks. Fixed rack domains; pair with IB/RoCE for inter-rack scale-out. Treat as the scale-up tier; use IB/Ethernet for scale-out.
Cross-DC AI Networking Inter-site fabrics for replication, DR and federated AI. Multi-region inference, geo-distributed data lakes, hybrid cloud AI. 100/400/800 G WAN Ethernet, DWDM, EVPN/VXLAN overlays. Active-active topologies; encrypted links; latency-aware engineering. Place caches/object gateways close to compute.
Juniper — EVPN-VXLAN Automated, standards-based DC fabrics for AI clusters. 3-stage/5-stage leaf-spine, multitenancy, validated designs. 400/800 GbE on QFX/PTX; EVPN-VXLAN; intent-based lifecycle automation. Design→deploy→operate with telemetry and assurance. Good fit for Ethernet-first AI fabrics.
Aruba — CX EVPN-VXLAN Leaf-spine DCN with lossless Ethernet options (RoCE/storage). DC fabrics, secure DCI, policy-driven segmentation. 25/100/200/400 G CX portfolio; EVPN-VXLAN; VSX for HA. Validated solution guides; capacity planning and best practices. Great for Ethernet-only shops.
Ubiquiti — Enterprise Aggregation Cost-effective L3 aggregation for campus/edge/labs. Edge AI labs, enterprise distribution; feeder to DC fabrics. Typical models: 48×25 G + 6×100 G; MC-LAG support; non-blocking design. Dual hot-swap PSUs and fans; simple management. Use upstream with DC-grade fabrics for training at scale.
Juniper Mist — Wi-Fi 6/6E/7 Intelligent wireless access with AI-driven operations. Campus/edge connectivity for users, IoT, and sensors feeding AI apps. Wi-Fi 7 (AP47/AP37/AP66), Wi-Fi 6E (AP64), Wi-Fi 6 (AP43/AP63), plus vBLE/UWB options. Cloud-native microservices; automated RRM; analytics and SLEs. Ideal for smart campuses and AI-assisted IT operations.

Quick Picks

1000+ GPU synchronous training

InfiniBand

Mid-scale training / large-scale inference

Ethernet + RoCE

Ethernet-only AI-grade consistency

NVIDIA Spectrum-X or Juniper Apstra EVPN-VXLAN

Inside the rack

NVLink/NVLink-Switch; use IB/Ethernet for inter-rack

Campus & edge wireless

Juniper Mist (Wi-Fi 7/6E/6) with Mist Cloud & Marvis AI

Need a fabric plan for your AI cluster?

We'll size your ports, choose the right fabric (InfiniBand vs RoCE vs Spectrum-X), and produce a topology with QoS, buffering, and congestion-control tuned for your GPUs.

Book a Consultation

Typical engagement: 45–60 min discovery → reference design & BOM → deployment plan.

AI Networking Solutions

Purpose-Built Infrastructure Components

Router

Routers

Multi-site connectivity for federated deployments

Learn more →
Firewall

Firewalls

Security & segmentation with zero-trust design

Learn more →
Switch

Switches

High-speed fabric interconnects for AI clusters

Learn more →
Access Point

Access Points

Intelligent Wi-Fi with AI-powered management

Learn more →

Resources & Downloads

Access our collection of whitepapers, brochures, and insights to help you make informed decisions.

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Brand:
DiGiCOR Brochure Brochure

DiGiCOR Brochure

Overview of infrastructure solutions: from GPU servers and AI workstations to scalable storage and edge systems.

DiGiCOR Download
Solution Overview

QuAI AI Developer Package

Build, train, and deploy AI models on QNAP NAS using GPU-accelerated computing and integrated AI frameworks.

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Build an AI Network
That Scales with Compute

From single-node deployments to distributed AI clusters, we design networking infrastructure that sustains performance under real-world AI workloads.

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