Roadmap

March 9, 2026 · View on GitHub

High-level overview of the main priorities for 2026

Support DRA for NVidia whole GPUs allocation
Support automatic sub-grouping of Ray, Pytorch and LWS
Block topology aware scheduling
Support scheduling placement strategy at the GPU level (currently supported at the node level)
Support K8S workload/pod-group API
Support Max Run Time per workload (with delayed requeue)
Max run time per queue (with delayed requeue)
Add metrics for pod and pod-group preemptions
User-level fairness
Support DRA for MIG devices
Support GPU compute sharing constraints
Support DRA for fractional GPU devices
Semi-preemptible workloads
Per queue multiple GPU types resource management

Refactor the codebase to enhance vendor neutrality https://github.com/kai-scheduler/KAI-scheduler/issues/134
Support Scheduling Gates https://github.com/kai-scheduler/KAI-scheduler/issues/63
Research on possible integration with Kueue https://github.com/kai-scheduler/KAI-scheduler/issues/68
Add Topology Aware Scheduling support of pod-group https://github.com/kai-scheduler/KAI-scheduler/issues/66
Support Min Run Time per workloads https://github.com/kai-scheduler/KAI-scheduler/issues/136
Add more PriorityClasses as part of the default KAI install
Support JobSet https://github.com/kai-scheduler/KAI-scheduler/issues/763
Support LWS (LeaderWorkerSet) https://github.com/kai-scheduler/KAI-scheduler/issues/124
Decouple Priority and Preemption
Specify fraction container name #654
Support n-levels of hierarchical queues #858
Add Time-based Fairshare #494

Add support for multi-cluster scheduling
Hyper scale improvements
Support Consolidation of Inference workloads for cluster defragmentation
Graceful rollout of Inference workloads (new revision update using queue temporary over-quota)
Support Hero Job
Support resource reservation and resources backfill
Support global priority scheme