Operations at AI Scale
AI infrastructure introduces operational challenges that standard application platforms were not designed for: GPU resource scheduling, model weight distribution, multi-tenant isolation on shared GPUs, inference-specific health checks, and cost attribution per model and team. The orchestration layer manages workload placement and lifecycle. The observability layer provides the metrics, logs, and traces needed to operate reliably, troubleshoot issues, and optimize costs.
Kubernetes GPU Scheduling
GPU-aware scheduling with NVIDIA device plugin, time-slicing for shared GPU access, and MIG (Multi-Instance GPU) for hardware-level isolation. Resource quotas per namespace prevent any team from monopolizing GPU capacity.
Prometheus + Grafana
Open-source monitoring stack collecting GPU metrics (DCGM exporter), inference metrics (vLLM/TGI), and application metrics. Grafana dashboards for operations, capacity planning, and executive reporting. AlertManager for PagerDuty/Slack integration.
Datadog / New Relic Integration
For organizations on commercial observability platforms, we integrate GPU and inference metrics into your existing dashboards. Single pane of glass for AI infrastructure alongside application and infrastructure monitoring.
Operational Maturity Matching
Design the orchestration stack to match your team capability. Docker Compose for simple single-node deployments. Kubernetes for multi-node clusters. Managed Kubernetes (EKS, AKS, GKE) to reduce operational burden.
Observability Stack Architecture
Instrument
Metrics, logs, and traces collection
Store
Time-series DB and log aggregation
Visualize
Dashboards and service maps
Act
Alerts, runbooks, and auto-remediation
Instrument
Metrics, logs, and traces collection
Store
Time-series DB and log aggregation
Visualize
Dashboards and service maps
Act
Alerts, runbooks, and auto-remediation
Orchestration & Observability
Kubernetes for AI Workloads
Kubernetes is the standard orchestration platform for containerized AI inference, but GPU workloads have unique requirements that require specific configuration and tooling.
NVIDIA GPU Operator. Automates GPU driver installation, container toolkit setup, device plugin deployment, and DCGM monitoring on Kubernetes nodes. Handles driver upgrades without node draining. Essential for any Kubernetes cluster running GPU workloads.
Multi-Instance GPU (MIG). A100 and H100 GPUs can be partitioned into up to 7 isolated GPU instances, each with dedicated memory and compute. Different models or teams get guaranteed GPU resources without interference. Kubernetes schedules workloads to MIG instances as if they were separate GPUs.
GPU time-slicing. When MIG is too coarse-grained, time-slicing shares a single GPU across multiple pods with temporal multiplexing. Lower isolation than MIG but more flexible allocation. Suitable for development environments and low-priority batch workloads.
Model weight caching. Large model weights (10-800 GB) must be available on every node that serves them. We configure shared PersistentVolumes (NFS, Lustre, or S3-backed CSI) so model weights are loaded once and shared across all pods on a node. Cold-start time drops from minutes to seconds.
Monitoring Stack Design
Effective AI monitoring requires metrics at four layers: hardware, inference engine, application, and business.
Hardware metrics. NVIDIA DCGM exports GPU utilization, memory usage, temperature, power draw, ECC errors, and NVLink throughput to Prometheus. Node-level metrics cover CPU, memory, disk, and network. These metrics identify hardware bottlenecks and predict failures.
Inference metrics. vLLM, TGI, and Triton expose request count, latency histograms, batch size distribution, KV-cache utilization, and queue depth. These metrics reveal whether the inference engine is efficiently converting GPU compute into useful throughput.
Distributed tracing. OpenTelemetry traces follow requests from API gateway through authentication, routing, queuing, inference, and response delivery. Trace data identifies which stage contributes the most latency for each request type.
Who This Is For
Orchestration and observability design is for organizations operating AI infrastructure at production scale. If you have multiple GPU nodes, multiple models, multiple consuming teams, or strict SLA requirements, the orchestration and monitoring layer is what makes reliable operations possible.
Contact us at ben@oakenai.tech