The outcome

Deploy only after measured traffic and quality evidence supports the dedicated GPU configuration.

Step by step

A workflow you can repeat.

  1. 01

    Define model license and revision, workload, quality, tokens, batch, throughput, latency, availability, privacy, logging, GPU, scaling, cost, and rollback targets.

  2. 02

    Benchmark the exact model through serverless APIs on representative traffic, capturing prompt length, output, errors, concurrency, tail latency, and cost.

  3. 03

    Create a dedicated test endpoint with scoped credentials, pinned repository revision, content logging disabled unless approved, conservative token and batch settings, and bounded replicas.

  4. 04

    Load-test cold start, autoscaling, memory, queueing, failures, output parity, privacy, observability, and spend, then compare with the serverless baseline.

  5. 05

    Canary the endpoint, monitor the version, preserve serverless fallback, document update and termination procedures, and promote only after owner approval.

Working standard

What good use looks like.

  • Pin the repository revision.
  • Disable content logging by default.
  • Keep the evaluated serverless fallback.

Official references

Check the current product documentation.