Category: AI/ML
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Air-Gapped Deployment, Lifecycle and CVE Patching for the NVIDIA Stack (NVIDIA AI Series, Part 15)
Running NVIDIA AI Enterprise in an air-gapped environment requires mirroring nvcr.io containers, Helm charts, and model weights before you cut the wire. Here is the branch selection, driver patch cadence, and CVE triage workflow that keeps regulated deployments defensible.
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NGC Catalog: Containers, Models, Helm Charts and How to Consume Them (NVIDIA AI Series, Part 14)
The NGC catalog is your upstream source for NVIDIA GPU-optimized containers, pretrained models, and Helm charts. Here is how the nvcr.io registry, org/team/API-key model, and NVAIE entitlement actually work, with a full operational pull-and-deploy walkthrough.
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NVIDIA GPU Operator on Kubernetes: ClusterPolicy, Components, and Day-2 Ops (NVIDIA AI Series, Part 12)
The NVIDIA GPU Operator automates every software layer a GPU node needs in Kubernetes, from kernel driver to DCGM metrics, via a single ClusterPolicy CRD. Here is what it installs, how the reconciliation loop works, when to disable the driver component, and the failure modes that will catch you on first install.
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InfiniBand vs Spectrum-X Ethernet: Choosing Your AI Cluster Scale-Out Fabric (NVIDIA AI Series, Part 8)
InfiniBand Quantum-X800 and Spectrum-X Ethernet both run at 800 Gb/s — but they are not the same choice. A direct comparison of SHARPv4 in-network reduction, lossless fabric mechanisms, rail-optimized topology, multi-tenant isolation, and operational trade-offs, with a clear verdict on which fabric wins for dedicated AI training versus shared enterprise GPU platforms.
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GPUDirect Storage: DMA From NVMe Straight to GPU Memory (NVIDIA AI Series, Part 9)
GPUDirect Storage (GDS) creates a direct DMA path from NVMe or networked storage straight into GPU HBM, bypassing the CPU bounce buffer entirely. Here is when it helps, what the cuFile API requires, and the filesystem and NIC prerequisites to validate before enabling in production.
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GPU Power, Cooling and Density: Why Blackwell Forces Liquid (NVIDIA AI Series, Part 10)
The GB200 NVL72 draws ~120 kW per rack and ships liquid-cooled by design. Learn why Blackwell-class systems make direct-to-chip cooling mandatory, how CDUs and facility water loops work, and what to validate before ordering.
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NVLink and NVSwitch: How NVIDIA Builds the Scale-Up Fabric (NVIDIA AI Series, Part 7)
Fifth-generation NVLink delivers 1.8 TB/s per GPU, and NVSwitch builds a non-blocking 130 TB/s all-to-all fabric across 72 GPUs in the GB200 NVL72. Here is how the domain forms, why it determines your tensor and expert parallelism strategy, and where the boundary falls.
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GPU Partitioning on NVIDIA Data-Center GPUs: MIG vs vGPU vs Time-Slicing vs Passthrough (NVIDIA AI Series, Part 6)
Four ways to partition an NVIDIA H100, H200, or B200 GPU: MIG, vGPU, CUDA time-slicing, and full passthrough. This post covers the isolation guarantees, profile geometry, Kubernetes GPU Operator configuration, and a sizing worked example to help you pick the right mode for your cluster.
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NVIDIA AI Factory Systems: DGX, HGX, MGX and NVL72 (NVIDIA AI Series, Part 5)
DGX, HGX and MGX are not performance tiers, they are three ways to integrate the same NVIDIA GPUs. Here is how they differ and where the GB200 and GB300 NVL72 rack actually earns its 120 kW.
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GPU Memory and Precision: HBM3e, HBM4 and What Actually Fits (NVIDIA AI Series, Part 4)
A 70B model in FP16 needs 140 GB of weights before a single token of context. Here is the GPU memory and precision math that decides what fits, why HBM (not FLOPS) is the real ceiling, and where FP8 and NVFP4 buy you headroom.
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NVIDIA Data-Center GPU Lineup: Hopper vs Blackwell vs Rubin (NVIDIA AI Series, Part 3)
The NVIDIA data-center GPU lineup from Hopper to Blackwell to Rubin, compared for training and inference: memory, bandwidth, FP4 and rack-scale NVL72, with a clear way to choose.
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NVIDIA AI Enterprise: What the Subscription Includes and What It Costs (NVIDIA AI Series, Part 2)
NVIDIA AI Enterprise is the supported, secured wrapper around the open-source NVIDIA stack, licensed per GPU. Part 2 covers what is in the box (NIM, NeMo, Run:ai, the operators), how it is licensed (subscription, consumption, perpetual), what it costs, and when it is worth it.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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