{"resourceId":"nvidia-runai-open-source-dsx-20261013","versions":[{"version":"external-ceb851cef0a1f6d0ff6a2b469047f8f69bdc8ae1adc2a60bfac98102bc26912b","resource":{"id":"nvidia-runai-open-source-dsx-20261013","title":"NVIDIA makes the full Run:ai GPU orchestration platform open source as part of DSX AI Factory","organization":"NVIDIA","sector":"Technology vendor","geography":"Global","publishedAt":"2026-10-13","publicationDate":"2026-10-13","eventDate":"2026-10-13","sourceName":"NVIDIA Technical Blog","sourceLabel":"Vendor technical blog","sourceUrl":"https://developer.nvidia.com/blog/nvidia-runai-is-now-open-source-as-part-of-nvidia-dsx-ai-factory-platform/","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["infrastructure","operating-model","governance-procurement"],"finding":"On Oct 13 NVIDIA released the complete Run:ai platform as open source, plus four separately adoptable components: KAI Scheduler (Apache 2.0, CNCF Sandbox), GPU Fractioning Operator (CUDA 13.4-native fractions; the proprietary implementation retired), KAI Resource Management (projects, departments, quotas, node pools) and Workload Map (a Go library). Eight DSX partners are integrating it: Crusoe, Mirantis, Nebius, OpenNebula Systems, Rafay, Saturn Cloud, Spectro Cloud and vCluster. The NGC Community Product requires accepting NVIDIA license terms and excludes enterprise support; paid editions continue through Mission Control and AI Enterprise.","sledRelevance":"High for research universities and state shared AI computing programs that pool GPUs across labs or agencies on Kubernetes.","evidence":"Vendor announcement; Model Streamer benchmark figures cited are vendor and partner measurements; no utilization results for the open release itself.","architectureImplications":"Kubernetes-native GPU scheduling, fractions and quotas; complements but does not replace Slurm-based HPC.","governanceImplications":"Departmental quota hierarchies can encode research governance decisions about who gets guaranteed GPU capacity.","securityPrivacyImplications":"Not addressed in depth; multi-tenant isolation depends on configuration and partner tooling such as per-tenant clusters.","caveats":"Vendor claims; utilization and throughput benefits asserted without data for this release; community edition unsupported.","streamIds":["nvidia"],"roles":{"sales":"Sales framing: NVIDIA made the full Run:ai GPU orchestration platform open source on Oct 13, adding four components (KAI Scheduler, GPU Fractioning Operator, KAI Resource Management, Workload Map) to its DSX AI Factory platform. For universities and state AI computing programs, this removes license cost as a barrier to testing GPU sharing and departmental quotas. Interpretation: lead with a scoped pilot on an existing Kubernetes GPU cluster, and be clear the free edition carries no NVIDIA support.","engineering":"Engineering framing: the blog describes gang scheduling, fair-share quotas, topology-aware placement and GPU sharing (MIG, MPS, time sharing, CUDA 13.4 fractioning), and says the old proprietary fractioning was retired for CUDA-native fractions. Pre-sales engineers should check CUDA 13.4 and Kubernetes version prerequisites and confirm that the NGC Community Product terms are acceptable to the institution's counsel before proposing it.","delivery":"Delivery framing: the release gives three paths: self-supported open source, eight named DSX partner offerings, and NVIDIA's paid supported editions. Delivery teams should decide the support model before deployment, because the free edition shifts upgrades, security patching and incident response to campus staff. Acceptance criteria below are proposals, not observed results."},"roleDetails":{"sales":{"customerProblem":"Shared campus and state GPU clusters suffer from idle reserved GPUs and queue conflicts between labs, and commercial orchestration licenses have been a barrier to fixing it.","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":["Is the GPU estate already on Kubernetes, or still Slurm on bare metal?","Who would own upgrades and security patching without a vendor support contract?","Can counsel accept the NVIDIA license terms attached to the NGC Community Product?"],"valueHypothesis":"Interpretation: a scoped pilot could raise GPU utilization for notebook and small-model workloads through fractions and quotas; the source reports no utilization figures for this release.","potentialEngagement":"Bounded pilot of open-source Run:ai or KAI Scheduler on one shared Kubernetes GPU cluster, with utilization measured before and after.","unsupportedClaims":["Any specific utilization or cost savings","That the free edition is production-supported","That it replaces Slurm for tightly coupled HPC jobs"]},"engineering":{"fit":"High for research universities and state shared AI computing that run Kubernetes GPU clusters; low where workloads are single-team or Slurm-only.","architectureIntegration":"Control plane plus per-cluster services on Kubernetes; KAI Scheduler, GPU Fractioning Operator (CUDA 13.4-native, scheduler-agnostic), KAI Resource Management for projects, departments and node pools; Workload Map as a Go library.","prerequisites":["Kubernetes GPU cluster with NVIDIA GPU Operator","CUDA 13.4 drivers for native fractions","Identity integration for role-based access"],"constraints":["Free edition excludes NVIDIA enterprise support","NGC download requires NVIDIA license acceptance in addition to open-source licenses","Commercial editions' terms vary by offering"],"security":"Shared multi-tenant clusters need tenant isolation review; GPU fractions isolate memory but share compute, so sensitive-data workloads need separate policy.","proofOfValue":["Measure utilization on a pilot cluster for four weeks before and after","Test fraction memory isolation with representative notebooks","Confirm a security-patch path for the community edition"]},"delivery":{"work":"Deploy and configure open-source Run:ai or individual components on an existing Kubernetes GPU cluster, mapping labs to projects and departments.","dependencies":["Kubernetes platform team","CUDA 13.4 driver rollout","Campus identity provider"],"ownership":"Research computing platform team, with the CIO's office accountable for the support model.","skillsAdoption":"Platform engineers need Kubernetes scheduler and GPU operator skills; researchers need training on requesting fractions and quotas.","governanceCheckpoints":["Support model decision before production use","Legal review of NGC Community Product terms","Fair-share policy approved by research governance"],"acceptanceCriteria":["Proposed: measured GPU utilization improvement on the pilot cluster versus baseline","Proposed: documented patch and upgrade procedure tested once"],"risks":["Unsupported software in production","Kubernetes skills gap","Policy disputes between labs over quotas"]}},"retrievedAt":"2026-10-14T02:56:44Z","enrichedAt":"2026-10-14T03:01:25Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Requires Kubernetes and GPU platform skills many campus teams lack.","procurementImplications":"Removes license cost for self-supported use; support must be bought from NVIDIA or partners or staffed internally.","operatingModelImplications":"Shifts from vendor-supported subscription to self-operated or partner-managed service.","sourceVerification":{"openedUrl":"https://developer.nvidia.com/blog/nvidia-runai-is-now-open-source-as-part-of-nvidia-dsx-ai-factory-platform/","referenceExcerpt":"No paid NVIDIA Run:ai subscription or NVIDIA support entitlement is required for the Community Product, and NVIDIA enterprise support is not included.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}