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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.
Passed 19 of 21 test cases with the skill, and 4 of 21 without it.
and 14 more in the full report
16 fixed4 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to install PyTorch GPU wheels via pip on a Jetson running JetPack 6 with CUDA 12.6 userland. Which index path on the Jetson AI Lab PyPI server corresponds to this environment?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends the jp6/cu126 subtree path on pypi.jetson-ai-lab.io. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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.
pip install failed" or "wrong CUDA / SM" after installing a generic ARM wheel.docker run or pip install for ML stacks on Orin or Thor.l4t-cuda containers on NGC — redirect to nvcr.io/nvidia/cuda (multi-arch).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.
| 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.
test cases passed, vs 4 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.