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 agentmods add skills/nvidia/model-optimizer/deploymentnpx skills add NVIDIA/Model-Optimizer --skill deploymentgit clone --depth 1 https://github.com/NVIDIA/Model-OptimizerWhat 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 | $0.00117 | $0.03555 |
| Opus 5 | $0.00059 | $0.01777 |
| Sonnet 5 | $0.00023 | $0.00711 |
| Haiku 4.5 | $0.00012 | $0.00356 |
Grade C, and why
deployment scanned grade C with 2 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 yesterday.
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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s http://localhost:8000/v1/models | python -m json.tool Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:8000/health How it starts
The opening of the file, as written. The whole thing — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deployment Skill
Serve a model checkpoint as an OpenAI-compatible inference endpoint. Supports vLLM, SGLang, and TRT-LLM (including AutoDeploy).
Quick Start
Prefer $SKILL_DIR/scripts/deploy.sh for standard local deployments — it handles quant detection, health checks, and server lifecycle. Use the raw framework commands in Step 4 when you need flags the script doesn't support, or for remote deployment.
# Start vLLM server with a ModelOpt checkpoint
"$SKILL_DIR/scripts/deploy.sh" start --model ./qwen3-0.6b-fp8
# Start with SGLang and tensor parallelism
"$SKILL_DIR/scripts/deploy.sh" start --model ./llama-70b-nvfp4 --framework sglang --tp 4
# Start from HuggingFace hub
"$SKILL_DIR/scripts/deploy.sh" start --model nvidia/Llama-3.1-8B-Instruct-FP8
# Test the API
"$SKILL_DIR/scripts/deploy.sh" test
# Check status
"$SKILL_DIR/scripts/deploy.sh" status
# Stop
"$SKILL_DIR/scripts/deploy.sh" stop
The script handles: GPU detection, quantization flag auto-detection (FP8 vs FP4), server lifecycle (start/stop/restart/status), health check polling, and API testing.
Decision Flow
0. Check workspace (multi-user / Slack bot)
If MODELOPT_WORKSPACE_ROOT is set, use the common skill's workspace-management.md. Before creating a new workspace, check the current session for existing model workspaces — especially if deploying a checkpoint from a prior PTQ run:
ls "$MODELOPT_WORKSPACE_ROOT/<session_id>/" 2>/dev/null
If the user says "deploy the model I just quantized" or references a previous PTQ, find the matching workspace and cd into it. The checkpoint should be in that workspace's output directory.
1. Identify the checkpoint
Determine what the user wants to deploy:
- Local quantized checkpoint (from ptq skill or manual export): look for
hf_quant_config.jsonin the directory. If coming from a prior PTQ run in the same workspace, check common output locations:output/,outputs/,exported_model/, or the--export_pathused in the PTQ command. - HuggingFace model hub (e.g.,
nvidia/Llama-3.1-8B-Instruct-FP8): use directly - Unquantized model: deploy as-is (BF16) or suggest quantizing first with the ptq skill
What ships with it
9 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.
- yesterday First seen · 277 lines · 117 tokens per session scan C 7decd6aab2c7
deployment is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,612 stars, last pushed yesterday), licensed Apache-2.0. It adds 117 tokens to every session and 3,555 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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