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

KubernetesvLLM

Explore GPU Infrastructure in the interactive universe or train with live cohorts.

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