Understand what to monitor across compute, GPU, network, storage and workloads.

Build a layered telemetry model across workload, scheduler, GPU, node, network and storage.

Metrics/logs/traces

Metrics show state over time, logs preserve discrete events and traces connect request paths where applicable. Infrastructure observability combines them with hardware telemetry and scheduler events rather than treating dashboards as an end in themselves.

GPU health

GPU health signals can include hardware errors, thermal or power state, driver/runtime events, device availability and workload-level validation. Alerting should distinguish transient noise from conditions that justify draining or repairing a node.

Compute

Compute telemetry includes CPU, memory, disks, kernel and process state alongside accelerator health. A GPU symptom can originate in host memory, PCIe, NUMA placement or another node-level constraint.

Network

Distributed AI is dominated by east-west traffic between accelerators and nodes. Learn leaf-spine architecture, routing, MTU, congestion, loss and telemetry before going deeper into RDMA, RoCE or InfiniBand. NVIDIA current reference architectures use dedicated high-bandwidth fabrics because network behavior directly affects distributed workload efficiency.

Storage

AI storage must serve large datasets, repeated reads, many metadata operations and checkpoint writes. Engineers reason about throughput, latency, metadata, caching, resilience and the interaction between storage traffic and the compute network.

Kubernetes

Learn containers, images, registries, pods, scheduling, services, storage and node constraints. Kubernetes exposes GPUs through vendor device plugins and schedules them as resources, but production GPU platforms still need driver, topology, health and quota management around that core.

Alerting

Alerts should indicate conditions requiring action, not merely unusual metrics. Route by ownership, include context and suppress predictable maintenance noise so engineers can trust the signal.

SLOs

Choose indicators that represent service behavior users care about, then define objectives that drive engineering decisions. For AI infrastructure this may include job success, queueing, node availability or platform API reliability rather than a single generic uptime number.

The strongest preparation for AI infrastructure observability is a combination of system understanding and inspectable evidence: a design note, lab, automation workflow, benchmark, incident analysis or capacity model that you can explain under questioning.

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