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Jetson Package

NVIDIAOfficialCreated by Jetson TeamDistributed by NVIDIA

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Pick Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes; maps Orin SM 8.7 vs Thor SM 11.0 and JetPack-specific package choices.
— the description by Jetson Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides best practices for identifying and installing software on NVIDIA Jetson devices. It emphasizes using curated artifacts from NVIDIA-AI-IOT and Jetson AI Lab to ensure compatibility with specific GPU streaming multiprocessor targets like sm_87 for Orin and sm_110 for Thor. By relying on device-specific package indexes and container registries rather than generic ARM64 sources, users can avoid performance issues and installation failures caused by missing GPU-native kernels.

What it does

  • Maps Jetson hardware generations to compatible container and package sources.
  • Provides guidance on selecting the correct Jetson AI Lab PyPI index for specific JetPack and CUDA versions.
  • Advises on using NVIDIA-curated container images over generic ARM64 alternatives.
  • Explains the necessity of matching software builds to the device's specific GPU compute capability.
  • Integrates with diagnostic scripts to detect device facts before recommending installation targets.

When to use it

  • When determining which container or Python package to install on a Jetson device.
  • When encountering installation errors with generic ARM64 wheels.
  • Before deploying ML stacks like PyTorch or vLLM on Orin or Thor hardware.
  • When a user needs to identify the correct CUDA or JetPack-specific package index.

Advantages

  • Prevents the use of incompatible or unoptimized software builds.
  • Ensures software is compiled for the correct GPU streaming multiprocessor target.
  • Directs users to canonical, NVIDIA-supported package repositories.
  • Reduces troubleshooting time by aligning software choices with detected device hardware.

Usage examples

“Which container should I use for vLLM on my Jetson Orin?”
The agent runs the detection script to identify the generation and recommends either the upstream vLLM image for newer JetPack versions or the specific GHCR image for older Orin setups.
“How do I install onnxruntime-gpu on my Jetson?”
The agent advises using the Jetson AI Lab index with the appropriate extra-index-url flag that matches the device's specific JetPack and CUDA userland.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/jetson-package/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip jetson-package.zip -d .agents/skills

Documentation by Jetson Team

Jetson Package & Environment

Agents often suggest docker pull images or pip install wheels that claim aarch64 support but were never built for Jetson’s GPU streaming multiprocessor (SM) targets. On Jetson, default to NVIDIA-curated artifacts unless the user explicitly opts out.

Purpose

Choose Jetson-compatible containers and Python package indexes before installing GPU-native ML stacks. This skill prevents agents from recommending generic ARM wheels or stale container tags that do not include the right CUDA, JetPack, or SM target for the device.

When to use

  • "Which Docker image / container should I use on this Jetson?"

  • "Where do I get PyTorch / vLLM / CUDA wheels for Jetson?"

  • "pip install failed" or "wrong CUDA / SM" after installing a generic ARM wheel.

  • Before docker run or pip install for ML stacks on Orin or Thor.

  • User or agent looks for l4t-cuda containers on NGC — redirect to nvcr.io/nvidia/cuda (multi-arch).

  • "Which PyTorch container should I use on Jetson?" — answer depends on Thor vs Orin and JetPack version.

Canonical sources (use these first)

  1. Prebuilt containers (GHCR) — NVIDIA-AI-IOT packages: llama_cpp, ollama, live-vlm-webui, older-Orin vllm, and related images built for Jetson JetPack stacks. Prefer these over random arm64 images on Docker Hub. For vLLM, use upstream vllm/vllm-openai on Thor and Orin JetPack 7.2 / L4T r39+.

  2. NGC CUDA / PyTorch containers — Tag selection depends on Jetson generation. Do not treat example PyTorch tag shapes as pinned recommendations; look up the current tag in the NGC PyTorch catalog before giving a command.

    JetsonCUDA basePyTorch
    Thornvcr.io/nvidia/cuda:<ver>-devel-ubuntu<ver> (multi-arch, arm64 included)nvcr.io/nvidia/pytorch:<current-tag>-py3 (main multi-arch tag; verify current NGC tag)
    Orin + r36 / JetPack 6same multi-arch CUDA basenvcr.io/nvidia/pytorch:<current-tag>-py3-igpu — verify the current NGC tag and use the -igpu suffix for Orin iGPU (SM 8.7) when NGC publishes it
    Orin + r39+ (future)samelikely main multi-arch tag once Orin becomes SBSA; verify when r39 ships

l4t-cuda is the legacy Orin-era CUDA container line. If a user cannot find l4t-cuda on NGC, redirect them to the current multi-arch nvcr.io/nvidia/cuda image instead of third-party images. 3. Python package indexes (devpi) — Jetson AI Lab PyPI: browse the tree (for example jp6/cu126, jp6/cu128) and pick the index that matches your JetPack / CUDA userland. Prefer these over PyPI-only wheels for GPU-native stacks.

GPU architecture reminder (why generic ARM fails)

Jetson familyCUDA compute capabilityBuild targetNote
Orin (AGX / NX / Nano)8.7sm_87Many desktop aarch64 wheels omit Jetson Orin kernels.
Thor (T5000 / T4000)11.0sm_110Requires CUDA / wheels / containers that include Blackwell Jetson support.

A wheel or container may install on ARM64 Linux and still be unusable or slow if CUDA kernels were not compiled for your Jetson’s SM.

Use CUDA build target names when discussing wheel compatibility: sm_87 for Jetson Orin and sm_110 for Jetson Thor. Do not infer the generation from a prompt or a hostname — run scripts/artifact_hints.sh and use its detected generation, variant, l4t, and cuda_sm_hint fields before recommending wheels or container tags.

GPU Python wheels on Jetson

Default PyPI wheels for GPU-native packages are usually not the right answer on Jetson, even when they claim aarch64 support. For onnxruntime-gpu, PyTorch, vLLM, and similar packages, use the Jetson AI Lab package index as the canonical source and choose the subtree that matches the device's JetPack / CUDA userland.

For onnxruntime-gpu, lead with Jetson AI Lab rather than plain PyPI:

pip install --extra-index-url https://pypi.jetson-ai-lab.io/jp6/cu126/+simple/ onnxruntime-gpu

Adjust the jp6/cu126 portion to match the detected JetPack / CUDA line. Do not present pip install onnxruntime-gpu from default PyPI as an equivalent Jetson GPU option.

Do not fabricate device facts

Do not invent SKU names, RAM sizes, JetPack versions, CUDA versions, or GPU SM targets. Quote only what scripts/artifact_hints.sh or the user's supplied environment reports. If a field is unavailable, omit it or say it is unknown.

Prerequisites

  • Run package-detection scripts on a Jetson target, not on the host workstation.

  • Network access is needed to inspect GHCR, NGC, or Jetson AI Lab package indexes.

  • Source device facts from scripts/artifact_hints.sh, jetson-diagnostic, or user-provided environment output before recommending tags or wheels.

Available Scripts

ScriptPurposeArguments
scripts/artifact_hints.shEmits detected Jetson SKU/generation, CUDA SM hint, canonical package URLs, and a preferred vLLM image hint.--human for a readable summary; no argument for JSON.

If your agent runtime supports run_script, use it to run scripts/artifact_hints.sh and read the JSON output. Otherwise run the script with bash from the repository root.

Instructions

  1. Run scripts/artifact_hints.sh (JSON on stdout). It sources skills/jetson-diagnostic/scripts/detect_jetson.sh and returns sku, generation, product_line, variant, l4t, a preferred vLLM image, cuda_sm_hint, and canonical URLs.

  2. For pip, open the devpi root in a browser, pick the jp6 subtree that matches your CUDA line, and set --extra-index-url / PIP_EXTRA_INDEX_URL — see references/pypi-jetson-ai-lab.md.

  3. For containers, see references/ghcr-images.md and jetson-llm-serve for vLLM.

Limitations

  • This skill points to package catalogs and emits compatibility hints; it does not verify that a specific model checkpoint fits in memory.

  • NGC and GHCR tags change. Treat placeholder tag shapes such as <current-tag>-py3 as lookup instructions, not literal tags.

  • If generation or cuda_sm_hint is unknown, do not guess a container tag.

Hand off to

  • jetson-llm-serve — run upstream/native vLLM 0.20+ on Thor and Orin JetPack 7.2 / L4T r39+, or vllm:latest-jetson-orin on older Orin.

  • jetson-llm-benchmark — measure after the stack is installed.

  • jetson-diagnostic — if installs succeed but runtime fails, snapshot first.

Safety

Read-only: points to catalogs and emits hints; does not install or pull.

Sources

NVIDIA-AI-IOT GitHub Packages, pypi.jetson-ai-lab.io.

Files

LICENSE

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Jetson Team
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/jetson-package · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Jetson Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download jetson-package.zip