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 compare-resultsgit 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/compare-results)<a href="https://agentmods.dev/skills/nvidia/model-optimizer/compare-results"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/compare-results/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/compare-results"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/compare-results.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 | $0.00109 | $0.01404 |
| Opus 5.5 | $0.00044 | $0.00562 |
| Sonnet 5.5 | $0.00022 | $0.00281 |
| Haiku 4.5 | $0.00011 | $0.00140 |
Grade A, and why
compare-results 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compare Results
Use this to plan and complete a baseline-vs-candidate comparison. The baseline is the reference checkpoint, and the candidate is the checkpoint whose accuracy change is being measured, typically a further quantized version of the baseline.
Workflow
- Establish the candidate checkpoint/run and the matching baseline. Infer the baseline from the PTQ source model/checkpoint in the workspace or config used to create the candidate. If it cannot be inferred, ask the user for the baseline checkpoint or an existing baseline invocation/run path.
- If a required baseline or candidate evaluation is missing, delegate to the evaluation skill to create, run, and verify it. The companion evaluation config should match benchmark versions, task configs, serving args, token limits, dataset setup, credentials, cluster, and container as closely as possible; change only the model/checkpoint and checkpoint-specific serving or quantization flags.
- Fetch the baseline and candidate task list, configs, score artifacts, and logs. If the user provides MLflow runs or invocation IDs, use the accessing-mlflow skill to fetch configs and artifacts.
- Confirm each run passed evaluation Step 9, "Verify completed evaluation run", before comparing scores. If not, validate logs, server health, judge/code-execution status, sample accounting, and reasoning parsing before computing deltas.
- For each task, use the canonical score field from the matching evaluation
skill task recipe,
recipes/tasks/<task>.md, under Score Extraction. - Use the evaluation skill's
references/run-validation.mdto perform the External Baseline Sanity Check. Record each source URL, protocol difference, and task status before applying the candidate-delta gate. A failed baseline blocks a success verdict; correct and rerun it first. If no credible comparable reference exists, label the baseline externally unverified rather than claiming the check passed, then continue using the validated measured baseline. - Compute exact deltas outside the chat context when there are multiple tasks or repeated runs.
- Report comparability, external baseline sanity, and quantized-feasibility verdicts before interpreting the delta as model quality. If the user did not provide an acceptance threshold, report feasibility as inconclusive instead of inventing one.
What ships with it
1 file 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.
- 2d ago Changed 9779b89cc01b
- 29d ago Changed · +9 lines d2a883a02798
- 1mo ago Changed 2c7a9ee8ac5d
- 1mo ago First seen · 103 lines · 109 tokens per session scan A 4dc94ba11245
compare-results is a skill published in the GitHub repository NVIDIA/Model-Optimizer (5,398 stars, last pushed today), licensed Apache-2.0. It adds 109 tokens to every session and 1,404 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.
Other skills, from other repositories
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…