The outcome

Produce a pinned serving configuration that meets measured output, latency, throughput, memory, stability, and cost targets.

Step by step

A workflow you can repeat.

  1. 01

    Define exact model revision and license, task, chat template, context distribution, output limits, quality and safety metrics, concurrency, latency, throughput, availability, hardware, and cost targets.

  2. 02

    Pin vLLM and container versions, verify model files and tokenizer, decide whether to accept model generation configuration, and record dtype, quantization, parallelism, memory, batching, and cache settings.

  3. 03

    Run a frozen correctness and safety set through the intended API, comparing tokenization, templates, sampling, structured outputs, tools, stop conditions, and output parity with the approved baseline.

  4. 04

    Load-test realistic prompt and output distributions through warm-up, steady state and spikes, capturing time to first token, inter-token latency, tail latency, throughput, queueing, GPU memory, errors, and cost.

  5. 05

    Choose conservative limits, archive the full manifest and results, soak-test, canary traffic, alert on saturation and output drift, and retain a prior model and configuration for rollback.

Working standard

What good use looks like.

  • Pin model, tokenizer and server versions.
  • Measure output quality under load.
  • Archive the full serving manifest.

Official references

Check the current product documentation.