Feature Overview (aka. Goal Summary)  

As an OpenShift administrator looking to run AI workloads on the platform, efficient GPU utilization is crucial given the high cost of these resources. While NVIDIA GPUs offer a method to pre-slice the GPU for multiple workloads, this approach can lead to resource wastage if the slicing does not align with the actual workload demands.

Therefore, I want to dynamically slice the GPU based on the specific requirements of each workload, ensuring optimal utilization and minimizing resource waste.

Goal 

TP in 4.19

Acceptance criteria 

• operator can run on infra/worker node
• Operator should not modify Machine config
• can be installed in non *openshift NS
• is build and tested via Konflux
• FIPS complient
• should work in disconnected mode

Non-Goals:

• Share MIG slices among multiple containers
• Achieve scheduling latency below 5s (need help from RH team)
• SNO/MicroShift testing is out of scope