Use Cases  ›  Enterprise Kubernetes

Enterprise · Fortune 500 · Industrial AI

Enterprise-Grade Kubernetes AI Orchestration

A Fortune-500 industrial AI team gave 100+ developers self-service GPU access in two weeks. Sub-second job-start latency. The 14-week manual ticket process was retired the same month.

Outcome at a glance

Job-start latency< 1s
Self-service developers100+
Ops tickets−85%
ArchitectureOn-Prem Single (HA pair)

Challenge

The platform team operated a 64×H100 cluster behind a manual ticketing process. Developers requested resources by email, the platform team allocated by spreadsheet, and budget reviews happened every quarter. Onboarding a new researcher took three weeks. Resource conflicts at the end of fiscal quarters caused production incidents.

Solution

CNLab installed against the existing Kubernetes cluster with no migration. RBAC roles mapped from existing AD groups. Quotas were derived from the spreadsheet (one-time import). The admin panel replaced both the ticketing process and the spreadsheet.

Results (8 weeks)

100+
self-service devs
< 1s
job-start latency
−85%
ops tickets
3 wks → 0
onboarding time

"Zero resource conflicts and sub-second job starts for 100+ developers. The scheduler does in real-time what we used to do in spreadsheets. The platform team stopped being a bottleneck the day we cut over."

— Platform Engineering Lead, Fortune-500 industrial AI division

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