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.

   Jetson	CUDA base	PyTorch
   Thor	nvcr.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 6	same multi-arch CUDA base	nvcr.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)	same	likely 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.

1. 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)

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

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.