Lambda integrates NVIDIA’s Open-Source Run:AI scheduler into its Kubernetes service
Lambda announced on October 13, 2026, that it is working with NVIDIA to integrate the open-source Run:ai scheduler as a managed option in Lambda Kubernetes Service (LKS). The scheduler, originally part of NVIDIA’s Run:ai platform, was open-sourced under the Apache 2.0 license in April 2025. Lambda has supported Run:ai since 2022 and aims to simplify adoption by embedding it directly into LKS,…
Key points
- Lambda and NVIDIA are integrating Run:ai scheduler into Lambda Kubernetes Service (LKS) as a managed option
- NVIDIA open-sourced Run:ai’s KAI scheduler in April 2025 under Apache 2.0 license for Kubernetes-native GPU scheduling
- LKS already includes auto-remediation for hardware faults and preinstalled NVIDIA GPU/Network Operators
NVIDIA’s scheduler enables GPU sharing, quota-based scheduling, and gang scheduling, dynamically adjusting resources in real time. It supports workloads like debugging, distributed training, and evaluations by dividing GPU memory into fractions, allowing multiple jobs to run concurrently. Lambda also highlighted auto-remediation in LKS, which detects and fixes hardware faults like XID errors or degraded NVLink links, ensuring stable GPU operations. The integration is part of Lambda’s broader plan to offer a managed library of schedulers, including Slurm and Run:ai, as validated components rather than requiring manual setup.
Lambda Readies Open-Source Run:ai Scheduler for Kubernetes Service
Unite.AI · 13 October 2026
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