Task · SKT-0087.19

Add GPU inference-serving engine telemetry (vLLM, Triton, NIM)

Description

Model-level metrics are usually owned by workload teams rather than the AI Factory platform team, but demos and workload-team stacks need inference serving on the same GPUs: request rate, queue time, time to first token, tokens per second, KV-cache usage and batch size. synthkit’s gen_ai constructs model hosted APIs and gateways, not self-hosted serving engines on GPUs.

Acceptance Criteria

Definition of Done

Implementation Notes

loop47: admitted R19 serving-engine vendor-contract research; implementation waits for DCGM and parked Runai exporter contract

loop47: source research completed at vLLM v0.30.0/Triton v2.73.0. vLLM core roots are model/engine aggregates, KV usage fraction and rates derived from counters; no invented per-GPU engine rate. NIM contract, health/failure event and GPU-fault-to-replica-removal mappings remain PENDING. Implementation not admitted: fixture and Runai/VAST dependencies not landed; no AC ticks.

loop47 dependency park, implementation0: DCGM and Runai consumers .04/.12 unlanded/source-blocked. R19 pinned vLLM0.30.0/Triton2.73.0 names/units retained; no NIM, health/failure-event or GPU-fault-to-replica mapping inferred. Resume after required consumers and eligible serving source profile land.

References

View the source file on GitHub