nvidia

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 […]

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

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: Quota establishes the resource entitlement for a

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

Introduction Enterprise AI architecture is often drawn from the compute layer outward. The GPU cluster sits in the middle, Kubernetes or virtual machines surround it, and storage appears as one cylinder at the bottom labeled data. That drawing is convenient, but it hides the design problem that causes many AI platforms to underperform or become