GPU Inference Platform

Prompt

Design a platform that lets teams deploy GPU-backed inference services reliably across multiple GPU pools.

Requirements

Functional:

• Deploy versioned model artifacts and runtime images.
• Serve online inference with p99 SLO.
• Support canary, rollback, and model warmup.
• Expose metrics, logs, traces, and GPU telemetry.
• Support multiple teams with quotas and isolation.

Non-functional:

• High availability.
• Safe rollout.
• Efficient GPU utilization.
• Fast incident diagnosis.
• Auditable changes.

Architecture

Control plane:

• Model registry and artifact store.
• Deployment API.
• Policy engine for quotas, resource envelopes, and allowed runtimes.
• CI/CD pipeline and promotion workflow.
• Scheduler integration through Kubernetes.

Data plane:

• Kubernetes clusters with GPU node pools.
• GPU Operator/device plugin/DCGM exporter.
• Model server such as Triton or equivalent.
• Service mesh or load balancer for traffic routing.
• Autoscaler based on request rate, queue time, and GPU saturation.

Observability:

• Request metrics by model/version/tenant.
• Model server metrics.
• GPU and node metrics.
• Deployment events.
• Distributed traces where useful.
• Incident dashboards aligned to failure layers.

flowchart TD
  subgraph ControlPlane[Control plane]
    Registry[Model registry]
    Policy[Policy and quotas]
    CI[CI validation]
    GitOps[GitOps promotion]
  end

  subgraph DataPlane[Data plane]
    Gateway[Gateway or mesh]
    Service[Inference service]
    Triton[Triton pods]
    GPU[GPU node pools]
    Repo[Model cache or repository]
  end

  subgraph Observability[Observability]
    Metrics[Request and Triton metrics]
    DCGM[GPU telemetry]
    Logs[Logs and events]
    SLO[SLO dashboards]
  end

  Registry --> CI
  Policy --> CI
  CI --> GitOps
  GitOps --> Triton
  Gateway --> Service
  Service --> Triton
  Triton --> GPU
  Repo --> Triton
  Triton --> Metrics
  GPU --> DCGM
  Metrics --> SLO
  DCGM --> SLO
  Logs --> SLO

Rollout Flow

1. Build immutable runtime image.
2. Register model artifact with checksum and metadata.
3. Validate resource envelope and health contract.
4. Deploy to staging/canary pool.
5. Warm model and run synthetic inference.
6. Shift small traffic percentage.
7. Gate on p99, errors, GPU memory, queue time, and model correctness checks.
8. Progressively expand.
9. Roll back by traffic shift and previous known-good version.

sequenceDiagram
  participant Dev as Model team
  participant CI as CI pipeline
  participant P as Platform policy
  participant G as GitOps
  participant K as Kubernetes
  participant O as Observability
  Dev->>CI: submit model and runtime
  CI->>P: validate contract
  P-->>CI: allow with envelope
  CI->>G: promote canary
  G->>K: reconcile deployment
  K->>O: emit health and SLO signals
  O-->>G: promote or rollback gate

Failure Modes

Failure	Mitigation
Bad model version	Canary, synthetic validation, fast rollback.
GPU memory pressure	Resource envelope, admission control, memory metrics, batch limits.
Node hardware/driver issue	Health automation, quarantine, pool segmentation.
Artifact bottleneck	Regional cache, prefetch, checksums, immutable artifacts.
Scheduler fragmentation	Pool-aware placement, defragmentation workflow, capacity model.
Tail latency spike	Queue metrics, batch tuning, autoscaling, admission control.

Tradeoffs

Central platform vs team-owned stacks:

• Central platform improves reliability standards and efficiency.
• Team-owned stacks can move faster for specialized workloads.
• Staff answer: provide paved roads with escape hatches, but require production contracts for any path.

Packing vs spreading:

• Packing improves utilization.
• Spreading reduces correlated blast radius.
• Use workload class and SLO to choose.

Autoscaling:

• Reactive scaling can be too slow due to model warmup.
• Combine predictive scaling, warm pools, and queue-based signals.

Closing Statement

The key is making model deployment boring. Every model version should declare its resource needs, health semantics, rollout policy, and observability. The platform enforces those contracts and gives operators enough evidence to act quickly when reality diverges.