GPU Clusters, HPC & AI Compute Infrastructure
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Job-Ready Certification CDI04
Large GPU fleets, distributed training/inference, scheduling, topology, NCCL/RDMA, throughput/latency, benchmarking, resource efficiency and failure recovery.
Benchmark: Target roles: AI Infrastructure Engineer | GPU Infrastructure Engineer | HPC / Compute Engineer -- Current market bar: Large GPU fleets, distributed training/inference, scheduling, topology, NCCL/RDMA, throughput/latency, benchmarking, resource efficiency and failure recovery. -- Credential note: this is a Zuyini job-readiness certificate built from the cited public courses. It is not issued, sponsored or endorsed by the source institutions, providers or companies.
Core Course Stack
1. Mastering LLM Inference Optimization
2. LLM Inference Optimization Explained
3. Machine Learning Explained
4. The Art of SLOs
5. AI Infrastructure on Kubernetes: Run:ai & KAITO
6. From Idle to Optimized: GPU Utilization at Scale
7. From Cluster to Factory: Scale AI Infrastructure Operations
8. Blackwell: The Journey From Die to Data Center
Role-Readiness Capstone
Benchmark and optimize a simulated GPU inference/training environment. Measure throughput, latency, memory pressure and utilization; test batching/quantization/scheduling choices.
Proof Rubric
- benchmark notebook
- utilization profile
- bottleneck analysis
- tuning experiments
- SLO/cost tradeoff
- failure/recovery test
- recommendation