{"resourceId":"github-dsx-ai-factory-run-ai-repo-readme","versions":[{"version":"external-ceb851cef0a1f6d0ff6a2b469047f8f69bdc8ae1adc2a60bfac98102bc26912b","resource":{"id":"github-dsx-ai-factory-run-ai-repo-readme","title":"NVIDIA Run:ai open-source repository: Apache 2.0 license, components, install paths and stated fit limits","organization":"NVIDIA (DSX AI Factory GitHub organization)","sector":"Technology vendor","geography":"Global","publishedAt":"2026-10-13 (repository public with the announcement; README undated)","publicationDate":null,"eventDate":"2026-10-13","sourceName":"GitHub","sourceLabel":"Vendor source repository documentation","sourceUrl":"https://github.com/dsx-ai-factory/run-ai","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["infrastructure","developers-agents","governance-procurement"],"finding":"The README describes a monorepo with the control plane, cluster services, installer, CLI, MCP server, client packages, scale tests and docs. It is licensed Apache 2.0 with trademarks excluded and nested MIT and CC-BY-4.0 components. It supports Kubernetes or OpenShift, connected or air-gapped, with images and Helm charts pulled from NGC (nvcr.io/nvidia/runai) by default. It says Slurm is an alternative for HPC-style batch on bare metal and that single-team dedicated GPUs may need only native Kubernetes. No long-range roadmap is published.","sledRelevance":"Moderate to high for university research computing teams deciding between Kubernetes GPU orchestration and existing Slurm clusters.","evidence":"Primary project documentation; does not include performance data.","architectureImplications":"Multi-cluster control plane; complementary to Kubeflow, Ray and Argo; alternative to Slurm for some workloads.","governanceImplications":"Role-based access and organizational policies across workloads.","securityPrivacyImplications":"Private vulnerability reporting; MCP server adds an AI-assistant control surface.","caveats":"Project self-description; support and end-of-life details sit on linked pages not reviewed line by line.","streamIds":["nvidia"],"roles":{"sales":"Sales framing: the Run:ai repository confirms the main license is Apache 2.0, with trademarks excluded and some vendored code under other licenses. It also states plainly where Run:ai does not fit: Slurm remains the alternative for HPC-style batch on bare metal, and single-team dedicated GPUs may need only native Kubernetes. Interpretation: that candor helps account teams qualify campus opportunities honestly.","engineering":"Engineering framing: the monorepo holds the control plane, cluster services, installer, CLI, an MCP server, and scale tests. It supports Kubernetes or OpenShift in connected or air-gapped installs, but released images and Helm charts are pulled from NVIDIA's NGC registry by default. Pre-sales engineers should check the support matrix and plan image mirroring for air-gapped research or government enclaves.","delivery":"Delivery framing: the repository publishes no long-range roadmap; planned work lives in open issues and releases, and a Support page lists versions and end-of-life dates. Delivery teams should build version tracking and upgrade windows into the operating plan. Acceptance criteria below are proposals."},"roleDetails":{"sales":{"customerProblem":"Campus and state IT teams need to know exactly what is open, what is licensed differently, and when Run:ai is the wrong tool before committing staff.","stakeholders":["University research computing / HPC director","Campus CIO and IT finance","Principal investigators sharing GPU clusters","State or system-wide shared AI computing program leads"],"discoveryQuestions":["Do you run HPC batch on bare metal with Slurm, Kubernetes, or both?","Do you need air-gapped installation?","Will you mirror NGC images internally?"],"valueHypothesis":"Interpretation: clear fit boundaries can shorten qualification and avoid failed pilots on Slurm-only estates.","potentialEngagement":"Bounded fit assessment comparing the institution's schedulers and workloads against the repository's stated scope.","unsupportedClaims":["That Run:ai replaces Slurm","That NVIDIA trademarks or brand assets are licensed","A published long-term roadmap"]},"engineering":{"fit":"Moderate to high for Kubernetes GPU estates; the README itself says low for single-team dedicated GPUs.","architectureIntegration":"Control plane managing one or more clusters; cluster services extend Kubernetes scheduling; web UI, CLI, REST API and an MCP server for AI assistants.","prerequisites":["Kubernetes or OpenShift within the support matrix","Access to nvcr.io or an internal mirror","Helm or the runai-installer CLI"],"constraints":["Default image source is NVIDIA NGC","Nested third-party licenses (MIT, CC-BY-4.0) in some subdirectories","No published long-range roadmap"],"security":"Vulnerabilities go through a private Security process; the MCP server exposes platform operations to AI assistants and needs access review.","proofOfValue":["Install in an air-gapped test cluster from mirrored images","Review the MCP server's permissions before enabling it","Check NOTICE files for redistribution obligations"]},"delivery":{"work":"Stand up a test install with the guided installer, verify license obligations, and set a version-tracking process.","dependencies":["Internal container registry","Kubernetes platform","Legal review of NOTICE files"],"ownership":"Research computing platform team.","skillsAdoption":"Teams need Helm, Kubernetes operator and release-management skills.","governanceCheckpoints":["Security review of MCP server exposure","License and NOTICE review before redistribution","Supported-version check before each upgrade"],"acceptanceCriteria":["Proposed: successful air-gapped install from mirrored images","Proposed: upgrade tested within a supported version window"],"risks":["Dependence on NGC availability","Version churn without a roadmap","Misreading Apache 2.0 as covering trademarks"]}},"retrievedAt":"2026-10-14T02:56:44Z","enrichedAt":"2026-10-14T03:01:25Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Builds run in containers, lowering local toolchain needs for contributors.","procurementImplications":"Apache 2.0 code permits self-use and modification; trademark and NGC terms remain separate.","operatingModelImplications":"Operators must track releases and supported versions themselves.","sourceVerification":{"openedUrl":"https://github.com/dsx-ai-factory/run-ai","referenceExcerpt":"Slurm is an alternative for HPC-style batch on bare metal.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}