GPU Infrastructure
GPU scheduling, memory, and compute for AI training and inference.
Maturity level: L2 — Can Build
Six perspectives on GPU Infrastructure
Roadmap
Advanced path — AI Infrastructure and senior MLOps.
Architecture
Compute layer under training and inference pipelines.
Company
Expected for AI infrastructure roles; growing in LLMOps.
Projects
Run distributed training or vLLM on GPU nodes.
Interview
GPU memory, scheduling, and cost optimization.
Career
Defines AI Infrastructure Engineer specialization.
What & Why
What: Graphics processing units used for parallel ML training and LLM inference.
Why: AI workloads are compute-bound — GPU infrastructure is the bottleneck at scale.
Build this
GPU-enabled training job with monitoring and cost tracking.
Production reality
- ! GPU fragmentation
- ! OOM
- ! Driver mismatches
- ! Spot instance preemption
Interview preparation
- GPU scheduling on Kubernetes
- Training vs inference GPU needs
Connected skills
Explore GPU Infrastructure in the interactive universe or train with live cohorts.
Want this GPU Infrastructure learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
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