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NVIDIA-Run-ai

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NVIDIA Run:ai

Type: Platform Tags: NVIDIA, Run:ai, GPU orchestration, workload orchestration, Kubernetes, AI Enterprise, MLOps, AI factory Related: NVIDIA-AI-Enterprise, NVIDIA-Run-ai-Support-and-Lifecycle, NVIDIA-Enterprise-Reference-Architectures, NVIDIA-AI-Enterprise-Software-Reference-Architecture, NIM-for-Large-Language-Models, NIM-for-LLM-Benchmarking-Guide, NVIDIA-NIM, NVIDIA-NIM-Operator, NVIDIA-RTX-PRO-AI-Factory, NVIDIA-HGX-AI-Factory, NVIDIA-NVL72-AI-Factory, NVIDIA-Mission-Control, NVIDIA-Base-Command-Manager, NVIDIA-DGX-SuperPOD, NVIDIA-GPU-Operator, KAI-Scheduler, NVIDIA-Grove Sources: https://docs.nvidia.com/run-ai/; https://docs.nvidia.com/run-ai/self-hosted/index.html; https://www.nvidia.com/en-us/software/run-ai/; https://docs.nvidia.com/mission-control/docs/systems-quick-start-guide/2.3.0/overview-runai.html; https://docs.nvidia.com/enterprise-reference-architectures/nim-llm-with-run-ai-and-vanilla-kubernetes.pdf Last Updated: 2026-04-29

Summary

NVIDIA Run:ai is NVIDIA’s AI workload and GPU orchestration platform for running AI workloads across the AI lifecycle. It manages GPU allocation, workload scheduling, policy, quota, governance, monitoring, and user/API workflows across hybrid infrastructure, with SaaS and self-hosted deployment paths. In current NVIDIA docs, Run:ai is part of the AI factory operations stack, appears in NVIDIA-AI-Enterprise guidance, and is integrated into NVIDIA-Mission-Control workflows for Kubernetes-native AI workload orchestration.

Detail

The current NVIDIA Run:ai docs split users into SaaS and self-hosted product paths. SaaS is NVIDIA’s fully managed cloud-hosted platform, while self-hosted is for on-premises and private cloud deployments aligned to cluster release cycles. Use NVIDIA-Run-ai-Support-and-Lifecycle for the current self-hosted support phases and version support dates.

Self-hosted docs cover install, setup, monitoring, organizations and resources, model building/training/deployment, APIs, workload assets, permissions, policies, health/performance monitoring, telemetry, and the Run:ai Scheduler. This makes Run:ai the canonical page for questions about NVIDIA-managed AI workload submission, quota, teams/projects, policy-driven GPU access, and multi-tenant workload operations.

The Enterprise RA NIM LLM sizing paper uses Run:ai with vanilla Kubernetes to improve GPU utilization for NIM-for-Large-Language-Models. It focuses on dynamic scheduling, autoscaling, fractional GPUs, and multi-model packing. The paper reports that full-GPU NIM inference performance with Run:ai is comparable to non-Run:ai runs and that fractional GPU NIM workloads can scale linearly in the tested setup.

Run:ai also anchors open-source scheduling work. NVIDIA’s product page links KAI Scheduler and Grove as open-source Run:ai ecosystem components: KAI-Scheduler for Kubernetes scheduling and NVIDIA-Grove for topology-optimized inference serving on Kubernetes. The same page mentions Run:ai Model Streamer as a model-loading acceleration feature, but this batch keeps it consolidated here until a separate current docs surface warrants its own page.

Connections

Source Excerpts

  • “NVIDIA Run:ai accelerates AI operations with dynamic orchestration across the AI lifecycle.”
  • “For on-premise and private cloud deployments.”
  • NVIDIA’s NIM LLM with Run:ai Enterprise RA paper uses Run:ai to pack more inference models onto a given set of NVIDIA GPUs through intelligent scheduling, fractional GPUs, and dynamic resource management.

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