GPU Platform Baseline for Production Model Serving

This lab documents a production baseline for GPU model serving. The goal is not a one-off notebook endpoint. It is a platform pattern: Kubernetes with GPU-aware scheduling, pinned serving runtimes, Git-backed promotion, and operations that connect latency to cost.

Teams usually arrive here after a successful demo. A model works in staging. Then concurrent traffic, multiple teams, and Friday releases expose the gaps: no quotas, no rollback, no shared observability, and GPU nodes treated as a shared free resource.

Most AI platforms struggle at the handoff from research to production. The serving path is informal, GPU capacity is contested, and nobody owns the promotion model when a release fails under load.

This baseline focuses on the platform layer that makes serving repeatable: how artifacts become endpoints, how GPUs are allocated, how changes move environments, and how operators know whether the problem is the model, the runtime, or the cluster.

The design centers on Kubernetes as the runtime fabric. GPU node pools are labeled by accelerator profile. Inference workloads declare real resource requests. Delivery is GitOps-driven: desired state lives in version control, and environments promote through staging and canary before full production.

Serving is pluggable. vLLM, Triton, and KServe are all valid choices depending on model type and team preferences. The baseline requires the same surrounding discipline regardless of which serving layer you pick: immutable artifacts, health checks, and a tested rollback path.

GPU platforms fail when scheduling is treated as an afterthought. Without node labels, taints, and realistic resource requests, batch jobs crowd out latency-sensitive inference, or one team exhausts capacity without anyone noticing until customers do.

A production baseline makes capacity explicit. Accelerator profiles are labeled. Workloads request the GPUs and memory they actually need. Quotas and PriorityClasses create boundaries between teams and between interactive serving and lower-priority batch work.

The serving layer should match the workload, not a fashion cycle. LLM endpoints often land on vLLM. Multi-framework or ensemble pipelines often land on Triton. Teams that want Kubernetes-native inference APIs often use KServe. The platform baseline stays neutral on that choice and strict on operations around it.

What matters is that every serving deployment is pinned, health-checked, and promotable. Autoscaling hooks into the same metrics you use for SLOs. Secrets and model credentials never live in the image.

A model is not production-ready if promotion means copying a tag by hand or SSHing into a node. The baseline uses CI to build immutable artifacts and GitOps to apply environment-specific desired state.

Staging exists to catch configuration and capacity mistakes. Canary exists to catch model and runtime mistakes under real traffic. Production only receives releases that have passed those gates, and the previous release remains available for rollback.

Cluster CPU dashboards are not enough. Inference platforms need signals that answer operational questions: is latency within SLO, is the queue backing up, is GPU memory pressured, and did this release change error rates?

Logs and metrics should be comparable across versions. Alerts should point to an action: pause a rollout, scale a deployment, cordon a node, or roll back a model package.

GPU spend is usually the largest variable cost in an AI platform. Autoscaling without limits turns a latency incident into a budget incident. The baseline pairs scaling policies with spend and capacity ceilings.

Scale on signals that matter for serving quality, not on vanity metrics. Prefer measured latency and queue pressure over raw request count alone. Keep a floor for cold-start sensitive workloads and a ceiling that finance can live with.

Production GPU platforms often start with one team and end with several. Identity, secrets, and network boundaries should be designed for that trajectory even if day-one tenancy is simple.

Service accounts, short-lived credentials, and least-privilege RBAC keep model artifacts and runtime credentials out of shared shells and notebooks. Network policy separates control plane, serving, and batch namespaces where risk warrants it.

This reference shows how Sailbird approaches cloud and hybrid GPU platforms: a small set of non-negotiables that make model serving operable before you chase every advanced feature.