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 quant-recipe-searchgit 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/quant-recipe-search)<a href="https://agentmods.dev/skills/nvidia/model-optimizer/quant-recipe-search"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/quant-recipe-search/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/quant-recipe-search"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/quant-recipe-search.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 | $0.00148 | $0.01915 |
| Opus 5.5 | $0.00059 | $0.00766 |
| Sonnet 5.5 | $0.00030 | $0.00383 |
| Haiku 4.5 | $0.00015 | $0.00192 |
Grade A, and why
quant-recipe-search 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quant Recipe Search
Use this skill when quantization is an iterative recipe search, not a one-off PTQ run. The skill owns strategy: define success, choose the search space, sequence candidates, and decide the next iteration. It delegates checkpoint generation, serving, evaluation, monitoring, and metric comparison to the existing execution skills.
Treat a direct request such as "find the best quantization recipe and generate a PTQ checkpoint for this model" as enough to start. Recover local state first, then ask only for missing decisions that change the search.
Skill Boundaries
- Use
ptqto produce and validate checkpoints. - Use
deploymentto serve checkpoints and debug serving-specific flags. - Use
evaluationto create NEL configs and submit evals. - Use
launching-evalsto run, resume, debug, and analyze NEL runs. - Use
monitorfor active job tracking. - Use
accessing-mlflowfor MLflow artifact lookup. - Use
compare-resultsfor validated baseline-vs-candidate deltas and score-field comparability.
Do not duplicate those workflows here. This skill should leave the user with a clear recipe portfolio, success metric, experiment sequence, and next decision.
Problem
The task is to find the best recipe for a user-defined target, not merely to produce a quantized checkpoint. A generated PTQ checkpoint is only a candidate. It becomes a recommended recipe only after evaluation and comparison against the matching baseline.
Required inputs before planning candidates:
- Optimization goal: compute/throughput, memory/latency, or a custom metric.
- Primary quantization family: for example NVFP4, W4A16 NVFP4, FP8/W8A8, INT4/AWQ, or a custom mixed set.
- Benchmark set or baseline results: the user-defined acceptance surface.
If any of these are missing, ask for them. Do not silently default to FP8/W8A8 or call a checkpoint "best" before evaluation.
Default success rule: maximize the chosen performance objective while keeping each benchmark within 1 percentage point of the matching BF16/FP16 baseline. Near-threshold or noisy regressions require reruns before making a decision.
What ships with it
2 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 First seen · 168 lines · 148 tokens per session scan A b7ea8395ff0c
quant-recipe-search is a skill published in the GitHub repository NVIDIA/Model-Optimizer (5,398 stars, last pushed today), licensed Apache-2.0. It adds 148 tokens to every session and 1,915 once invoked, about $0.0006 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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