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Nvidia tensorrt llm deployment review

Skill Raishin/vanguard-frontier-agentic/skills/nvidia/nvidia-tensorrt-llm-deployment-review

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.From its SKILL.md

Install
npx -y skills add Raishin/vanguard-frontier-agentic --skill nvidia-tensorrt-llm-deployment-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 20 stars20 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.9 KB, 462 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it: 1 file

1.7 KB alongside SKILL.md

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