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Kubernetes AI Workload Scheduling

Kubernetes AI Workload Scheduling

2
posts
2026

The platform now supports multiple orchestration layers for AI workloads, including Slurm for batch training, Kubernetes for flexible containerized workloads, SkyPilot for portable job execution, and dstack for vendor-agnostic management. This allows teams to align the platform with their specific requirements for scheduling, resource allocation, and configuration overhead.

2026

Choosing the right orchestration layer for your AI use cases

8/4/2026

This post compares four orchestration layers (Slurm, Kubernetes, SkyPilot, dstack) for AI workloads, detailing their pros and cons. It elaborates on Kubernetes' capabilities for AI workloads, including its extensibility with projects like Kubeflow, Ray, and Volcano, and mentions Lambda's support for managed and unmanaged Kubernetes with these components. It also references a previous post on comparing Kubernetes schedulers for AI workloads.

Why your Kubernetes scheduler can't handle AI workloads

7/16/2026

This post identifies and explains the limitations of the default Kubernetes scheduler for AI workloads, specifically the lack of gang scheduling and multi-node fabric topology awareness, leading to partial-scheduling deadlocks and performance throttling. It then details three distinct scheduling solutions: Kueue (queueing/quota management), KAI Scheduler (AI-native, topology-aware), and Volcano (mature batch scheduler), comparing their strengths, limitations, and ideal use cases. Finally, it outlines common deployment patterns and Lambda's role in assisting with scheduler selection and implementation.