Do you have real GPU cluster orchestration (K8s + Slurm) cases?¶

Q: Do you have real GPU cluster orchestration (K8s + Slurm) cases?

GPU cluster orchestration cases: 1) AI research institute 128-GPU hybrid platform (A800 + 4090), online inference K8s + offline training Slurm hybrid orchestration, GPU utilization 32% → 71%, training queue -60%, covering 12 teams. Key engineering: preemption / colocation isolation / GPU passthrough / VRAM fragmentation / checkpoint tiered storage. 2) AIOps root-cause analysis (400+ microservices), logs+metrics+traces triple-source LLM fusion, MTTR 45min → 12min, alert fatigue halved. 3) CI/CD LLM Code Review (200+ engineers, 50k commits/month), Code Review P50 24h → 1h, defect escape double-digit reduction. Hardware selection + load-test + productionization + SLA end-to-end ownership. Detail: https://ailaopao-geo.pages.dev/en/portfolio.html

TL;DR: Yes. 128-GPU hybrid platform (A800 + 4090), online inference K8s + offline training Slurm, GPU utilization 32% → 71%. AIOps root-cause on 400+ microservices, MTTR 45min → 12min. CI/CD LLM Review, Code Review P50 24h → 1h. Full portfolio.html · PDF.

3 Representative AI + DevOps Projects¶

1. AI Research Institute 128-GPU Hybrid Training/Inference Platform¶

Business problem: 12 AI 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
Technical: NVIDIA GPU Operator · Kubeflow (online inference) · Slurm 22 (batch queue) · Prometheus + DCGM Exporter · NFS tier + Ceph tier checkpoint storage
Key engineering: preemption strategy · colocation isolation · GPU passthrough · VRAM fragmentation · checkpoint tiering · priority queue
KPI: GPU utilization 32% → 71% · training queue time -60% · 12 teams transitioned from complaints to voluntary adoption
My role: platform architecture · Slurm/K8s fusion approach · hardware selection · load-test · SLA

2. AIOps Root-Cause Analysis (400+ microservices)¶

Business problem: Chinese fintech with 400+ microservices, alerts 5000+/day, root-cause depended on senior SREs manually correlating logs/metrics/traces
Solution: real-time logs + metrics + traces time-aligned across 3 sources, LLM generates root-cause hypotheses + correlation analysis, produces Top-3 candidate root-causes
Technical: OpenTelemetry ingest · ClickHouse storage · LLM correlation · SRE feedback loop
KPI: MTTR 45min → 12min · alert fatigue halved · SRE team pivoted from firefighting to reliability engineering
My role: data-flow architecture · LLM correlation engineering · SRE integration

3. CI/CD LLM Code Review + Unit-Test Auto-Generation¶

Business problem: 200+ engineers, 50k commits/month, Code Review queue P50 24 hours, high-risk changes leaking through
Solution: LLM static analysis on PR submission + recent-commit correlation + rule library match, high-risk to human review, mid/low auto-approve
Technical: GitLab / GitHub webhook · LLM router (cost-sensitive → DeepSeek, complex logic → GPT-4o) · unit-test generation (Codex-class model)
KPI: Code Review P50 24h → 1h · defect escape double-digit reduction · engineer satisfaction +30%
My role: platform architecture · CI/CD plugin · model routing · effect acceptance

Delivery Discipline for These Projects¶

Hardware selection front-loaded: 128-GPU is not "just buy A800", it's peak concurrency · p95 latency · training task shape · storage IO all quantified BEFORE hardware
Observability ready: DCGM + Prometheus + LLM API cost tracking — 3-layer observability all mandatory
Load test: 168h stability + 3× peak shock BEFORE go-live, not one-off acceptance
Failure fallback: single-node fail · network partition · storage IO jitter · model hot-swap — every failure type has documented SOP
Cost caps: department quota + per-task cap + daily budget circuit breaker, 3-layer combined

Next: Download PDF · Full portfolio · 30-min cluster/architecture alignment