Case 13 · 128-GPU Cluster K8s + Slurm Hybrid · Utilization 32% → 71% · Training Queue -60%

TL;DR: 128-GPU Cluster K8s + Slurm Hybrid · Utilization 32% → 71% · Training Queue -60%. Direction: AI + DevOps. Scale: 128 GPUs (A800 + 4090), 12 teams. Hard KPI: GPU utilization 32% → 71%, training queue time -60%. Tech: K8s (online inference) + Slurm 22 (offline training), NVIDIA GPU Operator, DCGM. My role: platform architecture, K8s+Slurm fusion, hardware selection, load test, SLA. Client name anonymized under NDA.

Problem

AI research institute with 12 teams sharing cluster, GPU utilization only 32%, training tasks queued 3-7 days.

Solution

K8s (online inference / short tasks / elastic) + Slurm (offline training / long tasks / preemptible) hybrid orchestration, unified GPU resource pool at bottom.

Tech Stack

K8s (online inference) + Slurm 22 (offline training), NVIDIA GPU Operator, DCGM

Result (Hard KPIs)

GPU utilization 32% → 71%, training queue time -60%

My Role

platform architecture, K8s+Slurm fusion, hardware selection, load test, SLA

Next: Download PDF · 30-min alignment

Client name anonymized · KPIs verified · Real client name disclosable under NDA.