How NVIDIA Built a $100M AI Factory in 4 Layers #NVIDIA #AIInfrastructure #GPU

This video details the NVIDIA AI Factory Cloud Provider Platform, highlighting its hardware, networking, and software layers. It illustrates the Kubernetes orchestration, tenant/consumer layer with GPU quotas and namespace isolation, and the monitoring and observability stack using Prometheus and Grafana. The NVIDIA AI Factory leverages advanced GPU technology within its data center infrastructure to power complex AI workloads.

NVIDIA has invested over $4 billion in AI Factory infrastructure — not chatbots, not apps, but the actual GPU-powered data centers underneath all of it.

This image breaks down the complete 4-layer architecture inside an NVIDIA AI Factory:

⏱️ WHAT'S COVERED:
Full Architecture Overview

🔷 Layer 1: Hardware
— DGX SuperPOD with GB300 racks (liquid-cooled)
— 72 Blackwell Ultra GPUs per rack
— NVLink 5 at 1.8 TB/s GPU-to-GPU bandwidth
— Quantum-X800 InfiniBand for RDMA
— 130 TB/s aggregate across 72-GPU domains

🔷 Layer 2: Kubernetes Orchestration
— GPU Operator (drivers, device plugin, DCGM, CDI)
— Run:ai KAI Scheduler (fairshare, gang scheduling, preemption)
— MIG Manager: 1 physical GPU → up to 7 isolated instances
— Network Operator for GPUDirect RDMA + InfiniBand
— Runs on Amazon EKS, vanilla Kubernetes, or OpenShift

🔷 Layer 3: Platform Software
— Base Command Manager (cluster provisioning)
— Mission Control (AI factory operations & automation)
— NVIDIA AI Enterprise (NIM, NeMo, Triton, RAPIDS, TensorRT)
— CUDA, cuDNN, NCCL underneath

🔷 Layer 4: Multi-Tenant Operations
— Namespace isolation per customer/team
— GPU quotas per tenant (e.g., 256 H100s for Tenant A)
— MIG slices or full GPUs per tenant
— DCGM Exporter → Prometheus → Grafana for monitoring
— Per-tenant usage tracking for chargeback billing

📊 Real-World Deployments:
— CoreWeave: $11.5B raised, building GPU cloud with this architecture
— Equinix: AI Factory access across 45+ global markets
— NTT DATA: Deployed for healthcare AI (cancer research) in March 2026
— Nebius: $2B NVIDIA investment for 5+ GW AI factory capacity

🔑 KEY TECHNOLOGIES:
NVIDIA DGX SuperPOD, Blackwell Ultra B300, GB300, NVLink 5, Quantum-X800 InfiniBand, Spectrum-X Ethernet, GPU Operator, Run:ai KAI Scheduler, MIG (Multi-Instance GPU), Base Command Manager, Mission Control, NVIDIA AI Enterprise, NIM Microservices, NeMo Framework, Triton Inference Server, RAPIDS, TensorRT, DCGM Exporter, Kubernetes, Amazon EKS

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#NVIDIA #AIFactory #Kubernetes #GPU #CloudComputing #AIInfrastructure #DGX #Blackwell #MLOps #DataCenter #tech #cloudcomputing #AIInfrastructure #MLInfrastructure #NVIDIAAIFactory #DGXSuperPOD #NVIDIAAIEnterprise #InfiniBand #Triton #Nemo #RAPIDS #TensorRT #CUDA #cuDNN #NCCL #NIM #DCGM #AIMonitoring #Monitoring #Prometheus #Grafana #NVLink #CloudAI #CloudInfrastructure

Видео How NVIDIA Built a $100M AI Factory in 4 Layers #NVIDIA #AIInfrastructure #GPU канала YV Labs by Vidh Yasa