NVIDIA Model Optimizer is a library that changes deep-learning models through techniques such as quantization, pruning, distillation, neural architecture search, speculative decoding, and sparsity so they can run more efficiently. Developers use it to optimize Hugging Face, PyTorch, or ONNX models and export checkpoints for inference frameworks such as SGLang, TensorRT-LLM, TensorRT, or vLLM. The catalogue entries provide skills, instructions, and a plugin for using its workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA/Model-Optimizer --skill run-agentxgit clone --depth 1 https://github.com/NVIDIA/Model-OptimizerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/model-optimizer/run-agentx)<a href="https://agentmods.dev/skills/nvidia/model-optimizer/run-agentx"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/run-agentx/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/model-optimizer/run-agentx"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/run-agentx.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.01278 |
| Opus 5.5 | $0.00015 | $0.00511 |
| Sonnet 5.5 | $0.00008 | $0.00256 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
run-agentx scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run AgentX
Setup
Resolve the endpoint URL, served model name, matching tokenizer, and requested
concurrency from the task. Reuse the model checkpoint. If serving is needed, follow
deployment to download missing weights and launch it.
Enable prefix caching, streaming usage, and cached-token reporting. For vLLM, use
--enable-prefix-caching --enable-prompt-tokens-details. Verify the context limit
fits the traces with the model's tokenizer; never silently truncate requests.
Start each benchmark campaign with the latest harness and compatible serving-stack releases unless the user requests specific versions. Record and pin the resolved harness and other code revisions, dataset/model/tokenizer revisions, serving framework (e.g. vLLM), and client/server dependencies or container digests in the run artifacts. Keep them fixed across sweep points, cache formats, and repeats unless they are the variable under test. Record local patches; treat upgrades as a new comparison baseline.
Install the latest AgentX harness
in a separate client environment. Set AGENTX_PYTHON to a Python executable
satisfying the latest release's Requires-Python metadata on
PyPI.
Installing upstream aiperf alone is insufficient.
set -euo pipefail
export AGENTX_WORKDIR="${AGENTX_WORKDIR:-$PWD/agentx-work}"
"${AGENTX_PYTHON:?}" -m venv "$AGENTX_WORKDIR/venv"
source "$AGENTX_WORKDIR/venv/bin/activate"
python -m pip install --upgrade agentx-harness
Set AGENTX_DATASET to an accepted with-subagents alias from the
AgentX tutorial.
Check the current tutorial for supported CLI options and scenario defaults.
The public loader downloads and caches the traces and prompt reconstruction assets.
An alias, even a dated one, does not select an immutable Hugging Face revision.
Resolve the alias's repository to a commit SHA and record both with every run.
Before benchmarking, verify that the loader passes that SHA as revision to
Hugging Face load_dataset. Without a revision flag, set the selected Weka loader's
hf_revision to that SHA in the campaign's client installation.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +15 lines 369d9b5e00ac
- 23d ago First seen · 91 lines · 38 tokens per session scan A 64a6c3149d52
run-agentx is a skill published in the GitHub repository NVIDIA/Model-Optimizer (5,398 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 1,278 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-29.
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