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 ptqgit 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/ptq)<a href="https://agentmods.dev/skills/nvidia/model-optimizer/ptq"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/ptq/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/ptq"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/ptq.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 | $0.00106 | $0.02890 |
| Opus 5.5 | $0.00042 | $0.01156 |
| Sonnet 5.5 | $0.00021 | $0.00578 |
| Haiku 4.5 | $0.00011 | $0.00289 |
Grade A, and why
ptq 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ModelOpt Post-Training Quantization
Produce a quantized checkpoint from a pretrained model. Read examples/hf_ptq/README.md first — it has the support matrix, CLI flags, and accuracy guidance.
Use quant-recipe-search for multi-candidate recipe exploration or
optimization. Use this skill for each selected recipe's PTQ run.
Step 1 — Environment
Use the common skill's environment-setup.md and workspace-management.md.
After completing them you should know:
- ModelOpt source is available
- Local or remote (+ cluster config if remote)
- SLURM / Docker+GPU / bare GPU
- Launcher available?
- Which workspace to use
Step 2 — Is the model supported?
Check the support table in examples/hf_ptq/README.md for verified HF models.
- Listed → supported, use
hf_ptq.py(step 4A/4B) - Not listed → read
references/unsupported-models.mdto determine ifhf_ptq.pycan still work or if a custom script is needed (step 4C)
Step 2.5 — Check for model-specific dependencies
If the model uses trust_remote_code (check config.json for auto_map), inspect its custom Python files for imports not present in the container:
grep -h "^from \|^import " <model_path>/modeling_*.py | sort -u
Known dependency patterns:
| Import found | Packages to install |
|---|---|
from mamba_ssm / from causal_conv1d |
mamba-ssm causal-conv1d (Mamba/hybrid models: NemotronH, Jamba) |
If extra deps are needed:
- Launcher (4B): set
EXTRA_PIP_DEPSin the task'senvironmentsection —ptq.shinstalls them automatically - Manual (4A):
unset PIP_CONSTRAINT && pip install <deps>before runninghf_ptq.py
Step 3 — Choose quantization format
First, check for a model-specific recipe:
ls modelopt_recipes/models/ 2>/dev/null
ls modelopt_recipes/model_type/<model_type>/ptq/ 2>/dev/null # per-arch; <model_type> from local config.json (Hub ID: AutoConfig.from_pretrained)
If a model-specific recipe exists, prefer --recipe <path> — but inspect its include/exclude patterns rather than assuming (e.g. for VLMs, confirm the vision tower is actually excluded).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed 7230c342abd7
- 1mo ago First seen · 215 lines · 106 tokens per session scan A 2cddd9d95def
ptq is a skill published in the GitHub repository NVIDIA/Model-Optimizer (5,398 stars, last pushed today), licensed Apache-2.0. It adds 106 tokens to every session and 2,890 once invoked, about $0.0004 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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