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 qadgit 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/qad)<a href="https://agentmods.dev/skills/nvidia/model-optimizer/qad"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/qad/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/qad"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/qad.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.01332 |
| Opus 5.5 | $0.00028 | $0.00533 |
| Sonnet 5.5 | $0.00014 | $0.00266 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
qad 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 1mo 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ModelOpt Quantization-Aware Distillation
QAD is expensive. Run it only when the user explicitly authorizes QAD for the target model or run. A Day-0, PTQ, evaluation, comparison, or recipe-search request alone is not authorization to start QAD.
Follow the supported workflow
Before constructing commands, read:
examples/megatron_bridge/README.md, especially PTQ, data preparation, QAD, export, and Slurm usageexamples/megatron_bridge/{quantize.py,distill.py}via--help- the common skill's
environment-setup.md,workspace-management.md, andslurm-setup.md; also itsremote-execution.mdfor remote Slurm
Treat the example README and --help output as authoritative for mutable flags,
commands, containers, and checkpoint formats. This skill supports Slurm only.
Execute in this order
- Confirm the gap. Reuse only validated, comparable BF16/PTQ results and the exact benchmark configuration from preceding evaluation or recipe search; run missing, invalid, or non-comparable baselines. Confirm the target benchmarks and their context-length needs. Stop if the PTQ gap to BF16 is already below 1%.
- Reproduce PTQ and verify compatibility. In the target runtime, require
AutoBridge.can_handle()for the target model and PTQ throughquantize.pyto succeed while preserving the exact preceding PTQ config or recipe: format, layer selection, calibration data/count, sequence length, and seed. A changed quantization setting is a new PTQ candidate and must be evaluated before QAD. In the master-rank.quant_summary.txt, require finite positiveamaxfor enabled static quantizers; acceptdynamic/format-definedNoneonly when the recipe intends it. Treat the summary as rank-local under model parallelism. - Choose topology explicitly. Derive the smallest fitting node count and
TP/PP/CP/EP from student and teacher architecture, the chosen sequence length,
and available GPU memory. Prefer CP before TP for small long-context models;
keep EP=1 for dense models and ETP=1 because the current
distill.pyworkflow does not support expert tensor parallelism. For MoE require:
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.
- 1mo ago First seen · 107 lines · 69 tokens per session scan A 53854abd981a
qad is a skill published in the GitHub repository NVIDIA/Model-Optimizer (5,398 stars, last pushed today), licensed Apache-2.0. It adds 69 tokens to every session and 1,332 once invoked, about $0.0003 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 2026-08-30.
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