Feature Summary:
The LeaderWorkerSet (LWS) API is designed for deploying and managing groups of pods as a unified replication unit, known as a "super pod." This capability is especially suited for AI/ML inference workloads, where large language models (LLMs) and multi-host inference workflows require sharded models across multiple devices and nodes. The LWS API allows OpenShift to manage distributed inference workloads, where a single leader pod coordinates multiple worker pods, enabling streamlined orchestration for complex AI tasks with high compute and memory demands.

Use Case:
For AI workloads that require distributed inference—such as LLMs or deep learning models with sharding across devices—LWS provides a structured way to orchestrate model replicas with both leaders and workers in a defined topology. This feature enables OpenShift users to deploy sharded AI workloads where models are divided across multiple nodes, providing the flexibility, scalability, and fault tolerance necessary to process large-scale inference requests efficiently.

https://github.com/kubernetes-sigs/lws 

https://github.com/kubernetes-sigs/lws/tree/main/docs/examples/llamacpp 

https://github.com/kubernetes-sigs/lws/tree/main/docs/examples/vllm/GPU 

Requirement for operator 

• • 1) disconnected 
  • 2) FIPS 
  • 3) Multi arch -> Arm 
  • 4) HCP -> ability to run operator in infra/worker node
  • 5) Konflux
  • 6) ability to deploy this operator in non openshift NS

Hypershift ROSA/ARO/OSD requirement -> for all operators

1. operator can run on infra/worker node
2. do not modify Machine config
3. can be installed in non *openshift NS
4. is build and tested via Konflux