Why Flat Networking is the Foundation of Modern AI Infrastructure
Artificial intelligence has pushed infrastructure design into a new era. What used to be “good enough” for cloud-native applications—VXLAN overlays, virtual switching, multi-tenant fabrics—is no longer sufficient for training and scaling Large Language Models (LLMs) or operating GPU superclusters.
AI workloads demand something different:
deterministic, lossless, ultra-low-latency networking.
This is why the industry is shifting from traditional overlay networking toward Flat L3 Fabrics.
Below is a complete explanation—supported by real diagrams—to help you understand why flat networking is becoming the foundation of modern AI infrastructure.
1. Traditional Overlay Networks: Designed for Cloud, Not AI
Overlays like VXLAN and Geneve were created to solve problems in cloud computing:
Multi-tenancy
VM mobility
Kubernetes networking
Network isolation at scale
But overlays come with overhead: encapsulation, CPU processing, jitter, and unpredictable latency. AI workloads cannot tolerate this.
Diagram: VXLAN Overlay Network Style
This is ideal for apps and microservices—but a barrier for GPUs that need synchronized communication.
2. Flat L3 Fabrics: Built for AI, HPC, and GPU Clusters
A Flat Fabric removes overlays and uses pure Layer 3 routing.
This creates a deterministic environment optimized for massive GPU communication.
A modern flat AI fabric uses:
BGP for routing
VRF for multi-tenant separation
RoCEv2 for direct memory-to-memory RDMA
PFC + ECN + DCQCN for lossless Ethernet
Leaf–Spine (Clos) topology for equal latency
Diagram: Flat L3 Fabric for AI
This structure ensures that every GPU sees every other GPU with the same latency, enabling efficient scaling from hundreds to thousands of GPUs.
3. GPU Communication: Why Latency Matters
GPUs do not operate like CPUs.
They perform collective operations during AI training, where all GPUs must exchange gradients in near real-time.
If one GPU slows down, the entire cluster slows down.
Diagram: GPU Communication Path (NVLink + RDMA)
AI networking is essentially part of the compute fabric—not just the transport layer.
This is why RDMA + Flat Fabric is mandatory for AI platforms like GPT, Grok, and LLaMA.
4. How Red Hat OpenShift Runs AI on Flat Networking
OpenShift is designed for cloud-native workloads, which normally rely on overlays.
But for AI workloads, OpenShift bypasses the overlay using:
SR-IOV (direct NIC access to pods)
Multus CNI (dual interfaces per pod)
RoCEv2 (AI data path)
GPU Operator (NVIDIA optimization stack)
This creates two independent network planes inside the same cluster:
| Plane | Used For | Technology |
| Application Plane | Apps, microservices, VMs | VXLAN / Geneve |
| AI Fabric Plane | GPUs, model training | RoCEv2 + Flat L3 Fabric |
Diagram: Dual Plane OpenShift AI Networking
Pods running AI workloads have two interfaces:
eth0 → Overlay (Kubernetes networking)
rdma0 → Flat Fabric (RoCEv2)
This ensures that OpenShift can support cloud-native AND AI-native workloads at the same time.
5. Two-Plane Data Center Architecture (Modern AI DC)
Every AI-ready data center now uses two simultaneous networks:
Application Plane (Overlay)
VXLAN/Geneve
Service Mesh
Kubernetes
Multi-tenant workloads
AI Plane (Underlay)
RoCEv2
Lossless Ethernet
BGP + VRF
Leaf–Spine Clos
GPU Superclusters
Diagram: Two-Plane Datacenter for AI
This dual-plane architecture is now standard for:
Oracle Cloud AI Superclusters
NVIDIA DGX SuperPOD
OpenAI and xAI GPU clusters
Red Hat OpenShift AI deployments
Meta AI Research (FAIR) clusters
6. Why Flat Networking is Non-Negotiable for AI
AI training at scale requires:
Lossless communication
Zero jitter
Deterministic latency
Direct GPU memory access
Horizontal scaling across racks
Overlay networks cannot provide this.
Flat fabrics do.
In One Line:
Flat networking transforms the network from a transport layer into part of the compute engine.
And that is why flat networking is the foundation of modern AI infrastructure.
7. How ComputingEra Helps Organizations Adopt Flat AI Networking
ComputingEra supports enterprises, banks, telecom operators, and government organizations by:
Designing AI-ready network fabrics (Flat L3 + RoCEv2)
Deploying OpenShift AI with dual-plane networking
Building GPU clusters for training and inference
Implementing sovereign AI platforms based on customer data
Integrating high-performance storage (NVMe-oF) with AI pipelines
Designing complete AI data center blueprints
Summary: Why Flat Networking Is the Foundation of Modern AI Infrastructure
As AI workloads grow in scale and complexity, traditional cloud networking models—built around VXLAN overlays and multi-tenant virtual networks—can no longer meet the performance demands of GPU superclusters and LLM training. Overlays introduce latency, jitter, and CPU overhead that destabilize synchronized GPU communication.
Flat Networking solves this by eliminating overlays and using a pure Layer 3 (L3) fabric built on BGP routing, VRF-based isolation, lossless Ethernet, and RoCEv2 (RDMA over Converged Ethernet). Combined with a leaf–spine architecture, this design provides predictable, ultra-low-latency communication across thousands of GPUs, making AI training more efficient, scalable, and cost-effective.
Modern AI platforms such as Oracle Cloud, NVIDIA SuperPOD, OpenAI, xAI, and Red Hat OpenShift adopt flat networking to ensure that GPU-to-GPU data exchange happens with minimal latency and maximum throughput. In Kubernetes environments, OpenShift uses a dual-plane approach: VXLAN/Geneve for applications and pods, and RoCEv2 with SR-IOV for AI workloads.
The result is a new datacenter architecture with two parallel network planes—an application plane for cloud-native workloads and a high-performance AI fabric for GPU clusters. Flat networking is now the essential foundation for training and serving modern AI models, and a critical design element for any enterprise building sovereign or high-performance AI infrastructure.