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jetson-package

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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.

When to use

• 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…
• "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.

When not to use

• 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.

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