La liberación de Run:ai por parte de NVIDIA bajo la licencia Apache 2.0 permite a los desarrolladores autoalojar y personalizar la programación de GPU en producción para Kubernetes sin necesidad de pagar una suscripción.
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NVIDIA has released the full NVIDIA Run:ai platform as open source software. The source code ships under the Apache 2.0 license on GitHub. NVIDIA Run:ai open source puts production GPU scheduling, quotas and workload management in developers’ hands. The release, makes Run:ai part of the NVIDIA DSX AI Factory platform. Platform teams can now inspect, fork and self-host the stack without a paid subscription. TL;DR Size: Monorepo with control plane, cluster services, installer, CLI and MCP server. Local dev builds about 50 Go services. Runs on: Kubernetes or OpenShift clusters with NVIDIA GPUs. Local dev runs on macOS or Linux with 8 simulated Tesla-A10 GPUs, no GPU needed. Performance: NVIDIA published no platform-level benchmark for this release. Best: The related Model Streamer loaded models 3x to 6x faster on 8 NVIDIA H100 VMs on Azure. Worst: The community edition has no NVIDIA enterprise support. Local builds need 16 GB RAM and 100 GB disk on Linux. What is NVIDIA Run:ai? NVIDIA Run:ai is an AI workload and GPU orchestration platform for Kubernetes from NVIDIA. It schedules development, training and inference workloads across shared GPU clusters. It runs on top of Kubernetes and does not replace it. The control plane handles workload submission, resource management and monitoring. Cluster services run on each Kubernetes cluster and add GPU-aware scheduling. Users reach it through a web UI, CLI, REST API and an MCP server for AI assistants. What are the 4 modular components? NVIDIA split the release into 4 components that teams can adopt independently: KAI Scheduler is the Kubernetes-native scheduling engine. It is Apache 2.0 and a CNCF Sandbox project. It covers gang scheduling, time-based fair-share, topology awareness, bin-packing and DRA support. GPU Fractioning Operator brings CUDA 13.4-native GPU fractions to Kubernetes. The CUDA driver enforces a hard memory limit per workload. NVIDIA retired its older proprietary fractioning in favor of this a
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