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)

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+.

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. 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 family	CUDA compute capability	Build target	Note
Orin (AGX / NX / Nano)	8.7	`sm_87`	Many desktop `aarch64` wheels omit Jetson Orin kernels.
Thor (T5000 / T4000)	11.0	`sm_110`	Requires 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:

bash

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

Script	Purpose	Arguments
`scripts/artifact_hints.sh`	Emits 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

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.

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
```
.

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.

jetson-package

NPX Install

Tags

SKILL.md Content