nvidia-tensorrt-llm-deployment-review

name: nvidia-tensorrt-llm-deployment-review description: Use this skill when reviewing TensorRT or TensorRT-LLM deployment artifacts statically — ONNX/PyTorch export pipelines, precision selection (FP16/BF16/INT8/FP8/INT4), calibration cache integrity, dynamic shape profiles, custom plugin loading, engine cache and serialized engine provenance, runtime memory pool sizing. Trigger when the user asks whether a TensorRT build script, calibration pipeline, or trtexec invocation follows NVIDIA's published guidance. allowed-tools: Read Grep Glob metadata: author: "github: Raishin" version: "0.1.0" updated: "2026-05-10" category: platform

NVIDIA TensorRT-LLM Deployment Review

Purpose

Static review of TensorRT and TensorRT-LLM deployment pipelines against NVIDIA's TensorRT Developer Guide — ONNX/PyTorch export, FP16/INT8/FP8/INT4 precision, calibration data integrity, dynamic shape profiles, plugin trust boundaries, engine cache provenance. This skill is doc-anchored: it grounds review findings in NVIDIA's published documentation rather than in a certification blueprint, because no NVIDIA certification currently covers this developer-facing surface as a standalone exam objective.

Lean operating rules

• Prefer the user's actual TensorRT build scripts, ONNX export code, and calibration pipelines as evidence; otherwise fall back to documentation-based inference.
• Treat custom TensorRT plugins loaded from non-pinned sources or unsigned object files as a critical finding — native-code execution surface inside the inference engine.
• Treat serialized engines (.engine, .plan) distributed without sha256 verification or provenance attestation as a high finding — silent model substitution.
• Treat INT8 / FP8 calibration data containing production user traffic without redaction or retention controls as a high finding — confidentiality and PII surface.
• Treat absence of optimization_profiles for variable input shapes as a medium finding — builds either fail at runtime or fall back to padded inference.
• Treat hardcoded --workspace or --memory-pool-size values that exceed the deployment GPU's free memory as a medium finding — engine build will OOM in CI.
• Treat use of --strict-types without explicit precision tagging on every layer as a low finding — actual precision drifts from intent.
• Always emit the exact trtexec, polygraphy run, or tensorrt_llm/build.py commands the user should run — do not execute them.

Response minimum

Return, at minimum:

• the scoped target (model source and export pipeline, precision selection and calibration posture, dynamic shape and profile posture, plugin and engine provenance posture, runtime memory and concurrency posture, recommended trtexec/polygraphy invocations) and evidence level,
• findings labelled critical / high / medium / low,
• recommended NVIDIA-tooling invocations the user should run themselves,
• safe next actions and assumptions or blockers.