An AI Factory is a purpose-built infrastructure platform designed to produce AI models, AI services, and AI outcomes at scale, similar to how a manufacturing factory produces physical goods.
AI Factories use layered architecture for the same reason hyperscale cloud providers do: separation of responsibilities, scalability, automation, multi-tenancy, and operational simplicity.
1- AI Factory Workloads Layer : Hosts the business and AI applications consuming GPU infrastructure.Such as LLM Training,Retrieval-Augmented Generation (RAG),AI Inference.
It Generate east-west GPU traffic,Consume storage and AI services & Leverage distributed GPU clusters. Eventually, It provides the compute demand driving the AI Factory.
2- Leaf-Spine Fabric Layer : Provides the high-bandwidth, low-latency Ethernet fabric interconnecting all AI infrastructure.Creates a scalable network fabric capable of supporting tens of thousands of GPUs.
3- Acceleration & Network Services Layer : It optimizes AI traffic patterns and ensures predictable application performance & Provides direct memory-to-memory communication between GPUs without CPU intervention, Prevents packet loss during AI training bursts.
It ensures deterministic network performance for AI applications by leveragning the services and features - RDMA over Converged Ethernet ,Congestion Control,Telemetry,Network Security & Analytics,Time Synchronization, Service Chaining.
4- Kubernetes & Container Orchestration Layer : Acts as the control plane managing containerized AI workloads.Cluster lifecycle management. It manages Pod scheduling,Resource allocation,High availability.
5- Operators Layer : NVIDIA GPU operator automates GPU management within Kubernetes. it performs function of automated installation and upgrades of drivers, detects available GPUs,provides GPU telemetry,creates GPU partitions (MIG Management). Final Outcome is , Transforms GPUs into cloud-native Kubernetes resources.
NVIDIA Network Operator Automates network configuration for Kubernetes AI clusters. It Integrates Kubernetes with physical networking,RDMA Enablement,Exports network metrics,Automates NIC and switch configuration,Ensures consistency across infrastructure.
Overall, Provides automated network lifecycle management.
6- SDN / Network Virtualization Layer : It Provides software-defined networking and tenant isolation such as Multi-tenant networking,Overlay networking,Policy-based routing,Network automation,Micro-segmentation. Eventually it Provides cloud-scale network virtualization.
7-GPU Workload Layer (Pods): It Hosts the actual AI training and inference workloads which has capabilities of RDMA communication,GPU Direct,Multi-GPU support,Tenant isolation.
8- Edge / Access Layer : It Connects compute, storage, and services to the fabric. Provides infrastructure access into the AI Factory.
9- Multi-Tenant Services Layer : It Enables secure sharing of AI infrastructure among multiple organizations, teams, or business units. it keeps isolation at Network , Kubernetes , Security , Observability. It Supports AI-as-a-Service operating models.
10 -Management & Operations Layer : Provides end-to-end operational visibility and automation.Provides Day-0, Day-1, and Day-2 operational capabilities.
End-to-End Traffic Flow :
AI Application → Kubernetes → GPU Pod → OVN SDN → NVIDIA Network Operator → Leaf Switch → Spine Fabric → Storage/GPU Cluster → Telemetry → NetQ → AI Operations Platform
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