docs: harden Jetson runtime decision

docs/platforms/jetson.md

@@ -42,17 +42,19 @@ versions to NVIDIA framework containers/wheels and JetPack versions. A generic
 ARM build can prove that Python code imports on `aarch64`; it cannot prove that
 CUDA, cuDNN, TensorRT, or PyTorch CUDA dispatch works on Jetson.
 
-## Initial support matrix
+## Proposed initial support matrix
 
 Start with the hardware reported in issue #31: Jetson AGX devices with 64 GB
 unified memory.
 
-Recommended first support tier:
+This is a planning target, not a current support claim. The implementation must
+replace the JetPack family with the exact patch installed on the available
+runner before publishing compatibility:
 
 | Tier | Hardware | JetPack | OS / CUDA baseline | Evidence requirement |
 | --- | --- | --- | --- | --- |
-| Target | Jetson AGX Orin 64 GB | 6.2 | Jetson Linux 36.4.3 / CUDA 12.6 | Native Jetson runner or NVIDIA Jetson container on Jetson hardware |
-| Evaluate | Jetson AGX Thor | 7.x | Jetson Linux 38/39 / Ubuntu 24.04 / CUDA 13.x family | Separate runner and issue before claiming support |
+| Initial candidate | Jetson AGX Orin 64 GB | 6.2.x, exact patch TBD | L4T/CUDA values from the selected patch | Native Jetson runner or pinned compatible container on Jetson hardware |
+| Evaluate later | Jetson AGX Thor | 7.x, exact release TBD | Select only after NVIDIA's PyTorch compatibility table covers the release | Separate runner and evidence before claiming support |
 | Legacy | Jetson AGX Xavier | 5.1.x | Jetson Linux 35.x / Ubuntu 20.04 / CUDA 11.x family | Defer unless a maintainer/user provides hardware and demand |
 
 Do not collapse these tiers into one "ARM64" support claim.
@@ -72,7 +74,10 @@ runtime image. A Jetson runtime should use one of these approaches:
 The lock policy should make the Jetson torch source explicit. The existing
 Linux PR lock intentionally uses CPU-only PyTorch. A Jetson install path needs
 an override or separate constraints file that preserves NVIDIA's Jetson PyTorch
-runtime.
+runtime. It must also prevent the generic PyPI Linux-aarch64 bitsandbytes wheel
+from being selected: upstream documents that wheel as SBSA/server ARM and says
+Jetson L4T/JetPack requires a source build. Until a pinned source build passes
+on the selected device, bitsandbytes is unsupported for that tier.
 
 ## Conditional gate
 
@@ -85,6 +90,9 @@ Add a new gate instead of modifying the x64 CUDA gate:
 - Evidence retention: same 30-day conditional-evidence policy as other hardware
   gates
 
+The job must run only from a trusted ref or reviewed maintainer dispatch. A
+persistent self-hosted Jetson must never execute untrusted pull-request code.
+
 The gate should verify:
 
 - `platform.machine()` is `aarch64` or equivalent ARM64.
@@ -94,8 +102,8 @@ The gate should verify:
 - OBLITERATUS resolves `device=auto` to `cuda`.
 - A small CUDA tensor operation completes with finite output.
 - The existing offloaded-surgery CUDA probe passes.
-- `bitsandbytes` NF4/4-bit quantization is either proven on that exact Jetson
-  stack or documented as unsupported for the tier.
+- A pinned, source-built `bitsandbytes` NF4/4-bit path is proven on that exact
+  Jetson stack, or bitsandbytes is documented as unsupported for the tier.
 - A tiny Hugging Face model run passes only when the model-download gate is
   explicitly selected and the runner has the required account/cache policy.
 
@@ -109,6 +117,9 @@ true:
   wheel source.
 - The Jetson conditional gate produces non-skipped green evidence on the exact
   commit being claimed.
+- The named `jetson-runtime` workflow job uses the documented runner labels and
+  retains `conditional-jetson-<run-attempt>` logs and environment metadata for
+  30 days.
 - The release notes distinguish generic ARM importability from Jetson CUDA
   support.
 - The docs state memory expectations for 64 GB unified memory and recommend
@@ -121,16 +132,17 @@ and file cache. Treat "64 GB" as a capacity class, not guaranteed usable model
 memory. Use small models for smoke tests, then move larger GPU validation to
 dedicated CUDA hosts such as Titan when those resources are available.
 
-Logging into a Hugging Face account is expected only for gated/private models,
-license-gated models, or rate-limit avoidance. It should not be required for the
-offline CPU PR gate or for the Jetson CUDA hardware probe. Any model-download
-validation must remain an explicit conditional gate.
+The Jetson hardware probe does not require a Hugging Face login. Model download
+testing remains the separate, explicitly selected `model-download-runtime`
+gate; credentials are relevant only when that selected model itself requires
+them.
 
 ## Sources
 
-- `REF-JETSON-PYTORCH-INSTALL`: NVIDIA, Installing PyTorch for Jetson Platform.
-- `REF-JETSON-PYTORCH-RELEASES`: NVIDIA, PyTorch for Jetson Platform release notes.
-- `REF-JETPACK-62`: NVIDIA, JetPack 6.2 release notes.
-- `REF-JETPACK-7-DOWNLOADS`: NVIDIA, JetPack SDK downloads and notes.
-- `REF-UV-PYTORCH`: Astral, Using uv with PyTorch.
-- `REF-BITSANDBYTES-INSTALL`: Hugging Face, bitsandbytes installation guide.
+- [NVIDIA: Installing PyTorch for Jetson Platform](https://docs.nvidia.com/deeplearning/frameworks/install-pytorch-jetson-platform/index.html)
+- [NVIDIA: PyTorch for Jetson compatibility table](https://docs.nvidia.com/deeplearning/frameworks/install-pytorch-jetson-platform-release-notes/pytorch-jetson-rel.html)
+- [NVIDIA: JetPack 6.2 release notes](https://docs.nvidia.com/jetson/archives/jetpack-archived/jetpack-62/release-notes/index.html)
+- [NVIDIA: current JetPack downloads and notes](https://developer.nvidia.com/embedded/jetpack/downloads)
+- [Astral: Using uv with PyTorch](https://docs.astral.sh/uv/guides/integration/pytorch/)
+- [Hugging Face: bitsandbytes installation guide](https://huggingface.co/docs/bitsandbytes/installation)
+- [GitHub: secure use of self-hosted runners](https://docs.github.com/en/actions/reference/security/secure-use#hardening-for-self-hosted-runners)