# jetson-llm-benchmark

Benchmarks LLM and VLM serving performance across vLLM, llama.cpp, and Ollama on NVIDIA Jetson, producing structured JSON outputs to compare tuning runs.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1200 installs · ★ 3735
- Author: Jetson Team
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-llm-benchmark/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/jetson-llm-benchmark

## What it does

This skill evaluates deployed model latency and throughput directly on NVIDIA Jetson hardware. Instead of invoking low-level benchmark commands manually, the agent uses dedicated wrapper scripts: `scripts/bench_vllm.sh` for active vLLM instances, `scripts/bench_llama_cpp.sh` for local GGUF weights, or `scripts/bench_ollama.sh` for the Ollama daemon. Every wrapper emits a standardized JSON payload to stdout.

The underlying scripts handle cache warmup passes and select NVIDIA-AI-IOT containers automatically for Orin and Thor systems. For GGUF files, the skill executes `llama-bench` inside a prebuilt container, so compiling from source is unnecessary. The vLLM script runs requests against the local server endpoint while discarding initial warmup prompts to avoid cold-cache distortion.

Benchmarks must run directly on the Jetson target device, preferably set to MAXN power mode. Ollama tests process requests strictly as a single stream via `/api/generate`, so their metrics cannot be compared directly against vLLM throughput numbers.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-llm-benchmark -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/jetson-llm-benchmark/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/jetson-llm-benchmark/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-llm-benchmark -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/jetson-llm-benchmark/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-llm-benchmark -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/jetson-llm-benchmark/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills jetson-llm-benchmark
```

2. **Manually: put the files in place**
- File: `.github/skills/jetson-llm-benchmark/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-llm-benchmark -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/jetson-llm-benchmark
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/jetson-llm-benchmark/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-llm-benchmark -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-llm-benchmark/SKILL.md

## When to use it
- measure latency and throughput for LLMs deployed on Jetson hardware
- run llama-bench on GGUF models using official containers without manual builds
- generate structured JSON metrics to compare memory tuning changes
- evaluate time to first token and generation speed across Ollama endpoints

## Example prompts
- Benchmark my running Ollama model using scripts/bench_ollama.sh
- Run scripts/bench_llama_cpp.sh against /path/to/model.gguf
- Measure vLLM serving throughput with scripts/bench_vllm.sh

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 934ec0122b7d230a.

BEGIN THIRD-PARTY FILE 934ec0122b7d230a
```text
---
name: jetson-llm-benchmark
description: Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.
version: 0.0.2
license: "Apache-2.0"
metadata:
  author: "Jetson Team"
  tags: [jetson, llm, benchmark]
  languages: [bash]
  data-classification: public
---

# Jetson LLM Benchmark

Reproducible Jetson benchmarks with **structured JSON output** so an agent can compare runs. Encodes the workflow from the [Jetson AI Lab GenAI Benchmarking tutorial](https://www.jetson-ai-lab.com/tutorials/genai-benchmarking/).

## Purpose

Measure deployed LLM latency and throughput on a Jetson target using the correct
runtime-specific benchmark wrapper. Use the JSON output to compare models,
runtime flags, power modes, and before/after tuning changes.

## Prerequisites

- Run on the Jetson device that hosts the model runtime.
- For vLLM, start the OpenAI-compatible vLLM server first and know the served
  model ID.
- For Ollama, ensure the Ollama daemon is reachable at `--endpoint` and the
  named model is already pulled.
- For llama.cpp/GGUF, provide a readable `.gguf` model path on the host.
- Put the device in the intended power mode before measuring. MAXN is preferred
  for comparable performance numbers.

## Available Scripts

| Script | Purpose | Arguments |
|--------|---------|-----------|
| `scripts/bench_vllm.sh` | Runs `vllm bench serve` against a running OpenAI-compatible vLLM server. | `--model`, `--endpoint`, `--concurrency`, `--input-len`, `--output-len`, `--num-prompts`, `--no-warmup`, `--container`, `--native`. |
| `scripts/bench_llama_cpp.sh` | Runs `llama-bench` for a local GGUF model through the Jetson-appropriate NVIDIA-AI-IOT llama.cpp container. | `--model`, `--n-prompt`, `--n-gen`, `--n-gpu-layers`, `--threads`, `--container`. |
| `scripts/bench_ollama.sh` | Benchmarks a local or containerized Ollama daemon through the `/api/generate` REST API. | `--model`, `--endpoint`, `--num-prompts`, `--input-len`, `--output-len`, `--no-warmup`. |

If your agent runtime supports `run_script`, invoke the selected wrapper directly with the user-provided model identifier or local model path, then summarize the returned JSON. Otherwise run the wrapper with `bash {baseDir}/scripts/<wrapper-name> ...`.

## Instructions

Always use the matching wrapper script for the runtime — do **not** call the underlying `vllm bench serve`, `llama-bench`, or `curl` against `/api/generate` by hand:

- vLLM → `scripts/bench_vllm.sh` (required for the vLLM path)
- llama.cpp / GGUF → `scripts/bench_llama_cpp.sh` (required for the GGUF path)
```
END THIRD-PARTY FILE 934ec0122b7d230a

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