Trace-first benchmarking
Every request becomes JSONL evidence, including TTFT, TTFB, ITL, latency, stalls, statuses, and workload metadata.
Black-box LLM inference profiler
A local CLI that records OpenAI-compatible inference traces, diagnoses latency failures, generates static reports, replays workload shapes, and fails CI when a model deployment regresses.
Every request becomes JSONL evidence, including TTFT, TTFB, ITL, latency, stalls, statuses, and workload metadata.
Runs are grouped by profile and concurrency, then explained with evidence-backed labels like TTFT Pekka and Queue Queen.
Baseline and candidate traces can be compared with explicit thresholds, giving automation a clean pass/fail contract.
pip install inference-autopsy
autopsy generate-fake --output examples/traces/fake.jsonl
autopsy report examples/traces/fake.jsonl --html report.html
The public report is generated from synthetic traces, so it is safe to share and does not expose prompts, endpoints, or API keys.