NVIDIA

Enterprise NVIDIA guidance for GPUs, AI factories, inference, Kubernetes, private AI, networking, infrastructure design, and operations.

How to Design and Validate an NVIDIA GB200 or GB300 NVL72 AI Factory

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TL;DR An NVIDIA NVL72 AI factory is not a collection of GPU servers connected to a fast Ethernet network. Each rack is a tightly coupled, 72-GPU NVLink scale-up domain with rack-level firmware, power, cooling, management, and service dependencies. Additional racks are joined through a separate scale-out fabric, commonly a dual-plane, rail-optimized Spectrum-X design for current … Explore: How to Design and Validate an NVIDIA GB200…

How to Enable NVIDIA GPUDirect Storage on Kubernetes

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TL;DR NVIDIA GPUDirect Storage, usually shortened to GDS, can move data between supported storage and GPU memory without routing the payload through a CPU bounce buffer. On Kubernetes, enabling the feature is not only a Helm setting. A working deployment depends on a supported GPU and operating system, the NVIDIA open GPU kernel module, a … Explore: How to Enable NVIDIA GPUDirect Storage on Kubernetes

How to Configure GPUDirect RDMA and Prove Multi-Node GPU Performance with NCCL

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TL;DR A working GPU driver, an RDMA device inside a pod, and a completed NCCL test do not prove that GPUDirect RDMA is working efficiently. Production validation must prove the complete path: GPU topology, GPU-to-NIC affinity, PCIe peer access, IOMMU and ACS behavior, RDMA fabric health, container resource exposure, NCCL transport selection, and repeatable multi-node … Explore: How to Configure GPUDirect RDMA and Prove Multi-Node…

How to Build an NVIDIA Spectrum-X Ethernet Fabric for an AI Factory

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TL;DR An NVIDIA Spectrum-X fabric should not be approached as a conventional Ethernet refresh with faster switches. Distributed AI creates synchronized, high-bandwidth traffic patterns in which congestion, packet loss, path imbalance, and tail latency can slow an entire training job. A production design should: The most important architectural principle is simple: build the network as … Explore: How to Build an NVIDIA Spectrum-X Ethernet Fabric…

How to Migrate NVIDIA GPU Scheduling from Device Plugins to Kubernetes DRA

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TL;DR Kubernetes Dynamic Resource Allocation changes NVIDIA GPU scheduling from an opaque integer request into an explicit device-selection workflow. Instead of asking only for nvidia.com/gpu: 1, a workload can claim a class of GPU, filter by architecture or memory, request full GPUs or MIG devices, and let the scheduler bind a specific device through a … Explore: How to Migrate NVIDIA GPU Scheduling from Device…

How to Deploy NVIDIA Dynamo on Kubernetes for Distributed LLM Inference

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TL;DR NVIDIA Dynamo is preferable to a standalone inference server when the serving problem extends beyond one process or one GPU node. It introduces a Kubernetes-native control plane for distributed inference graphs, separate prefill and decode workers, KV-cache-aware routing, model loading, topology-aware placement, autoscaling, fault recovery, Gateway API integration, and multi-node execution. This tutorial uses … Explore: How to Deploy NVIDIA Dynamo on Kubernetes for…

GPU Scheduling Is a Business Policy Problem: Designing NVIDIA Run:ai Quotas, Fairness, and Preemption

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Introduction A GPU cluster does not know which product launch is contractually committed, which research experiment can wait until tomorrow, or which inference endpoint supports a revenue-producing application. Kubernetes sees pods, resource requests, labels, and scheduling constraints. The business sees customers, deadlines, budgets, risk, and service commitments. That gap is where many shared GPU platforms … Explore: GPU Scheduling Is a Business Policy Problem: Designing…

NVIDIA NIM vs Triton vs vLLM: Choosing an Enterprise Inference Runtime Without Benchmark Theater

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Introduction Enterprise inference standardization often begins with a benchmark chart. That is usually where the first mistake occurs. A team runs one model on one GPU configuration, increases concurrency until the system saturates, records the highest tokens-per-second result, and declares a winner. The result may be technically repeatable while still being operationally irrelevant. It says … Explore: NVIDIA NIM vs Triton vs vLLM: Choosing an…

How to Configure Multi-Tenant GPU Scheduling with NVIDIA Run:ai

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TL;DR NVIDIA Run:ai can turn a shared Kubernetes GPU cluster into a governed multi-tenant platform by organizing workloads into departments and projects, assigning guaranteed GPU quotas per node pool, and allowing controlled over-quota use when capacity would otherwise remain idle. The design depends on four controls working together: Production workloads should normally run from quota-backed … Explore: How to Configure Multi-Tenant GPU Scheduling with NVIDIA…

How to Optimize NVIDIA Triton Inference Server for Throughput and Latency

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TL;DR NVIDIA Triton Inference Server performance tuning is not a matter of enabling dynamic batching and increasing model instances until GPU utilization rises. The correct process is to define a latency objective, establish a repeatable baseline, test realistic concurrency and arrival patterns, inspect queue and compute time separately, and then promote only configurations that improve … Explore: How to Optimize NVIDIA Triton Inference Server for…

How to Add NVIDIA NeMo Guardrails to a Production LLM Endpoint

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TL;DR NVIDIA NeMo Guardrails should sit in the controlled request path between the application and the production LLM endpoint, not beside it as an optional validation service. The application sends requests through an authenticated gateway to the guardrails service. Input rails inspect the request, retrieval rails inspect RAG context, execution rails constrain tool use, the … Explore: How to Add NVIDIA NeMo Guardrails to a…

How to Deploy NVIDIA vGPU on VMware vSphere and Validate the Configuration

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TL;DR Deploying NVIDIA vGPU on VMware vSphere is not simply a matter of installing a driver and attaching a virtual PCI device. The server, physical GPU, ESXi build, NVIDIA vGPU release, guest operating system, vGPU profile, and licensing model must all form a supported combination. A reliable deployment sequence is: The most important lifecycle rule … Explore: How to Deploy NVIDIA vGPU on VMware vSphere…

How to Monitor NVIDIA GPUs with DCGM Exporter, Prometheus, and Grafana

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TL;DR NVIDIA GPU monitoring needs more than a utilization chart. A production design should collect device telemetry with DCGM Exporter, scrape it with Prometheus, visualize fleet and workload behavior in Grafana, and alert on conditions that require action. The runbook must also preserve per-pod context, control metric cardinality, distinguish low utilization from genuine performance problems, … Explore: How to Monitor NVIDIA GPUs with DCGM Exporter,…

How to Install and Configure VMware NSX with an NVIDIA Spectrum Network Fabric

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TL;DR A reliable VMware NSX deployment on NVIDIA networking depends less on clicking through the NSX Manager wizard and more on getting the physical underlay right first. The NVIDIA Spectrum fabric must provide stable Layer 3 reachability between every ESXi and NSX Edge tunnel endpoint, consistent jumbo MTU, predictable uplink behavior, and resilient routing to … Explore: How to Install and Configure VMware NSX with…

GPU Multi-Tenancy Without Security Theater: Isolation, Quotas, Noisy Neighbors, and Confidential Computing

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Introduction GPU sharing is easy to describe and difficult to govern. A platform team can expose one physical accelerator as several scheduler-visible resources, divide it into Multi-Instance GPU partitions, present virtual GPUs to virtual machines, or assign the entire device to one workload. Kubernetes can place those workloads into separate namespaces. An enterprise scheduler can … Explore: GPU Multi-Tenancy Without Security Theater: Isolation, Quotas, Noisy…

Who Used the GPU? Building Per Tenant Telemetry, Showback, and Capacity Evidence for AIaaS

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Introduction A shared GPU platform creates a deceptively simple question: who used the GPU? The question becomes difficult as soon as the platform supports more than one operating model. A Kubernetes pod may receive an entire GPU, a MIG instance, or a time-sliced share. A virtual machine may receive a vGPU profile or a pass-through … Explore: Who Used the GPU? Building Per Tenant Telemetry,…

How Do You Protect a GPU-Backed Workload When Snapshots and Mobility Have Limits?

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TL;DR A GPU-backed workload is not recoverable merely because its virtual disks were copied or its Kubernetes manifests were committed to Git. vGPU, MIG-backed vGPU, PCI passthrough, Enhanced DirectPath, RDMA, and bare-metal GPU configurations expose different snapshot, suspend, migration, and failover boundaries. Some support controlled mobility within a narrow compatibility matrix. Others deliberately trade those … Explore: How Do You Protect a GPU-Backed Workload When…

Whole GPU, Passthrough, vGPU, MIG, or Time Slicing? The Enterprise GPU Allocation Decision Matrix

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Introduction Enterprise GPU design becomes confused when several different decisions are compressed into one question: “How should we share the GPU?” That question mixes hardware assignment, virtualization, Kubernetes scheduling, tenant isolation, business priority, and service-level commitments. The result is often a platform that advertises many GPU “slices” but cannot explain what each slice guarantees. A … Explore: Whole GPU, Passthrough, vGPU, MIG, or Time Slicing?…

How Many AI Workloads Can My GPU Platform Really Support?

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TL;DR An expected workload count is not a GPU requirement. Thirty concurrent notebooks, RAG services, inference endpoints, fine-tuning jobs, or distributed training runs can create radically different demands for GPU memory, accelerator time, CPU, system memory, storage throughput, metadata operations, network bandwidth, scheduling policy, and failure reserve. Capacity planning should begin by defining workload service … Explore: How Many AI Workloads Can My GPU Platform…

How to Deploy the NVIDIA RAG Blueprint on Kubernetes with Helm

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TL;DR The NVIDIA RAG Blueprint is not one application pod. It is a coordinated retrieval platform that combines an ingestion service, a RAG server, NVIDIA NIM microservices, NV-Ingest, a vector database, object storage, model caches, and supporting Kubernetes operators. For the current 2.6.0 release, Elasticsearch is the default vector database and SeaweedFS is the default … Explore: How to Deploy the NVIDIA RAG Blueprint on…