perf-host-analysis

To identify host CPU overhead in TensorRT-LLM inference, analyze nsys traces to measure GPU idle ratio, cudaLaunchKernel launch overhead, and GPU utilization. This confirms whether host-side operations are actively harming throughput.

Diagnosing TensorRT-LLM performance regressions involves comparing baseline versus target nsys traces to quantify per-step wall-time regressions. This produces an NVTX per-operation breakdown isolating scheduling and request-management overhead.

Yes, you can separate context and generation phase behavior when profiling TensorRT-LLM. This phase-aware diagnosis isolates eager versus graph replay behavior to reliably surface regressions masked in aggregate metrics.

Extracting iteration boundaries from an nsys SQLite trace relies on identifying allreduce and NVTX patterns. You then compute steady-state metrics and optionally intersect NVTX host-prep ranges with GPU idle gaps for exposed-amount attribution.

Host overhead analysis supports optional multi-rank straggler drill-down for TensorRT-LLM. When NVTX indicates time inside GPU dispatch, it further analyzes inter-kernel gaps, eager-vs-graph coverage, and repeating patterns across ranks.