Kubernetes Efficiency
GPU pool feasibility memo
Measured guidance for teams experimenting with inference pools without over-provisioning scarce accelerators.
What the briefing covers
We review queueing, sharing modes, and maintenance windows. The memo highlights where human review beats automation during early phases.
Feature checklist
- Queue depth observations with anonymized workloads
- Sharing mode comparison for your stack
- Maintenance window impact table
- Thermal and power notes if colocated hardware exists
- Activity log template for allocation changes
- Partner checklist for hardware procurement-ready requests
- Risk coverage callouts for long-running jobs
Outcomes
- Go / no-go memo with conservative ramp plan
- Owner list for monitoring gaps
- Quarterly revisit triggers
Lead editor
Yuri Cho
See Kubernetes saturation pass for background.
FAQ
No; we focus on infrastructure placement and sharing, not ML accuracy.
Desk notes from teams
The GPU pool feasibility memo stopped us from buying the wrong partition size. Still want more on driver pin versions, but overall crisp.