Profile a Model

Choose the evidence level before running:

• For one model or testcase, use trtmc-bench.
• For a release comparison against the native model, use trtmc-aiperf-qual (apps/aiperf_qual).

The public benchmark boundary is public_task_call_wall. It excludes bundle building, process startup, warmup, report generation, and bundle loading. Current public tooling does not provide layer attribution; do not infer a kernel or layer bottleneck from Task API wall time alone.

Before timing

Run the owning family correctness testcase on the exact bundle. Record the repository and checkpoint revisions, GPU and driver, runtime root, family DSO, operation, input, seed, warmups, iterations, and synchronization boundary.

For a focused run:

trtmc-bench run --model <model> --case <case> \
  --bundle <bundle> --no-build \
  --warmup 3 --iterations 10 --runtime-root <runtime-root> \
  --output <result-dir>

Use the same bundle kind, hardware, input, seed, warmups, iterations, and runtime policy for before/after comparisons. If any differ, report the confounder instead of one causal speedup percentage.

For release evidence, run the exact profile with trtmc-aiperf-qual run --profile <profile> --environment <environment> --out <result-dir> (see apps/aiperf_qual/README.md).

Lead the report with correctness and evidence level. Include exact commands, p50 and other suite-owned statistics, measurement scope, limitations, and any target not run.

HTTP text load evidence

For text serving measurements, reuse the persistent trtmc-server through python3 -m trtmc_aiperf_qual text-profile --environment <environment> --config <text-config> --out <result-dir> with PYTHONPATH=apps/aiperf_qual. Consult website/docs/user-guides/profile-text-with-aiperf.md and the checked-in apps/aiperf_qual/config/text/ examples. Keep the owning family's exact-bundle correctness checks before timing; HTTP success does not replace them.

Require incremental capability before interpreting TTFT or inter-token latency. Nonstreaming native records use public_task_call_wall; streaming records include relay/backpressure and have a different scope. Preserve warmup and failures when joining AIPerf exports to server records with X-Request-ID. Native prompt counts are unavailable; client tokenizer counts are estimates. AIPerf request latency ends at the last content response. Inspect the client request lifecycle and server handler timing as well: terminal delivery or cleanup can delay the next request without appearing in content latency. The optional sequential reference comparison requires matching payloads, outputs, token counts, precision and Task boundaries. It does not change the qualification criteria or establish speedup from HTTP latency alone.