Alpamayo2 Super

---
name: alpamayo2-super
description: Package, preflight, run, validate, or troubleshoot NVIDIA Alpamayo 2 Super 34B VLA inference in NPA, including its OpenMDW model terms, separately gated PhysicalAI-AV dataset, public runtime-fetch image, B200/RTX PRO 6000 routing, workflow artifacts, and real-GPU evidence.
---

# Alpamayo 2 Super

Run NVIDIA's real VLM plus 2.3B diffusion expert. Never replace inference with
an import, fabricated trajectory, or manifest-only smoke.

## Legal gates

Keep the boundaries separate:

- Source: `NVlabs/alpamayo2@beb2977d9a7e9d66837d4a3ad5144ff59de37519`,
  Apache-2.0, baked with its license and the marked dataset-revision patch.
- Model: `nvidia/Alpamayo2-Super@00554695e729a6ff0b6281fd2c81b18d06e33dbe`,
  OpenMDW-1.1. Acceptance is by exercising granted rights. Fetch at runtime.
- Dataset: `nvidia/PhysicalAI-Autonomous-Vehicles@b719eea7f0a63619ef51ec7f54178af0937ef050`,
  gated by NVIDIA's AV Dataset License. It is non-transferable; accept it
  interactively on Hugging Face and fetch it only into the operator cache.
- Outputs: OpenMDW-1.1 imposes no output restriction, but Alpamayo is not an
  automotive-grade driving stack. Preserve model-card safety provenance.

Before provisioning, run:

```bash
npa workbench health preflight
npa workbench health access --capability alpamayo2-super
```

A missing dataset entitlement is terminal. Open the exact URL printed by the
access command; Hugging Face acceptance cannot be automated by NPA.

## Build and scan

Build only from the checked-in Dockerfile. Never pass `HF_TOKEN` as a build arg
or populate `/workspace/.cache/huggingface` during a build.

```bash
bash npa/docker/workbench/alpamayo2-super/build.sh
npa/.venv/bin/python npa/scripts/scan_image_alpamayo2_payload.py \
  <exact-local-image>
```

Require a complete clean scan over every layer for checkpoints, PhysicalAI-AV
payload, caches, and credentials. Inspect SBOM/provenance separately; the byte
scanner is not a license review.

## Run the workflow

Use B200 first. The workload is headless and does not need RT cores; its measured
H100 peak is 72,115 MiB, so one B200 has ample memory. Validate RTX PRO 6000
separately because B200 `sm_100` does not prove RTX `sm_120`.

```bash
npa workbench workflow validate-spec \
  workflows/testing/alpamayo2-super-inference.yaml
npa workbench workflow submit \
  workflows/testing/alpamayo2-super-inference.yaml \
  --infra <configured-infra-target> --var bucket=<operator-bucket> \
  --secret-env HF_TOKEN --secret-env AWS_ACCESS_KEY_ID \
  --secret-env AWS_SECRET_ACCESS_KEY
```

The run must publish non-empty `trajectory.json`, `trajectory.png`, and
`result.json`. Verify exact model/dataset revisions, the runtime image digest,
`weights_baked=false`, `dataset_baked=false`, and the node-local ephemeral cache
tier. Treat success without all three artifacts as failure.

## Diagnose

For HTTP serving, configure `NPA_ALPAMAYO2_SUPER_TOKEN` as a deployment secret
and `NPA_ALPAMAYO2_SUPER_OUTPUT_ROOT` as an operator-owned local `0700`
directory or authorized S3 prefix. All operational routes require bearer
authentication; keep transport private or terminate HTTPS. HTTP clients can
select samples and inference controls, but cannot change the pinned model,
dataset revision, or startup snapshot of `NPA_ALPAMAYO2_SUPER_MANIFEST`.
Their `output_path` is a result label, and the server creates a fresh prefix
under its configured root. Trusted CLI/SDK customization remains available.
Keep the container-native health check on
`npa.workbench.alpamayo2_super.healthcheck`; it reads the admission credential
from the runtime environment and authenticates `/health` without putting the
credential in process arguments or output.

- 401/403 before GPU allocation: accept the dataset agreement with the same HF
  account or replace the rejected token; do not add an NPA bypass boolean.
- CUDA OOM: confirm one trajectory sample, ten diffusion steps, and no competing
  process; do not silently reduce cameras or frames.
- `no kernel image`: inspect Torch arch flags and flash-attn build targets.
  B200 requires `sm_100`; RTX PRO 6000 requires `sm_120`.
- Missing camera projection: retain fail-closed `--require-camera-projection`.
- Pending job: inspect scheduling and image-pull evidence. Change GPU products
  only for scheduler/capacity evidence and record a separate architecture result.

Cancel the exact run before teardown. Never delete shared operator resources
merely because a validation job finished.

## Accepted release baseline

`0.1.0-cu128-r3` is the accepted runtime-fetch baseline. Its OCI index digest is
`sha256:17a3966a6e743cf34ecaeb2ef684272646c815d07a8a4668ccebf17de6aa0e07`.
It was built from source commit `5b693476c113c833e9d9d4f8c7aa492492a27505`,
which includes the `HTTPConnection` loopback healthcheck fix. Later acceptance
metadata commits are not the image source. On 2026-09-16 the exact digest
completed the real upstream workflow on B200 (`sm_100`) and, independently,
RTX PRO 6000 (`sm_120`). The 25-layer payload scan and complete
vulnerability/secret/license scan were clean. Both runs
produced all three required artifacts, projected shape `[1, 1, 1, 64, 3]`, and
runtime-only model/data provenance. B200 measured ADE 1.503835 and FDE 4.357265;
RTX measured ADE 1.501321 and FDE 4.351557. Neither r3 run recorded a trustworthy
peak allocation, so retain NVIDIA's 72,115 MiB H100 measurement as the
conservative documented reference rather than inventing a B200 or RTX number.

For RTX qualification, preserve the committed B200 default and override the
submitted accelerator with `NPA_WORKFLOW_GPU_ACCELERATOR=RTXPRO6000:1`. Require
the same three artifacts and provenance checks; do not infer RTX support from a
B200 result.

## Verify changes

For Ray scenario/seed/diffusion experiments, use
`workflows/testing/alpamayo2-ray-sweep.yaml` and
`workflows/testing/alpamayo2-ray-hardcases.yaml`, derived from the inference
template. Accepted release `0.1.0-cu128-r3` contains
`npa workbench alpamayo2-super sweep` and Ray 2.58.0 in the NPA interpreter.
Use `--stage-src` only to test intentionally newer source. GPU actors reuse
downloaded snapshots while upstream subprocesses reload weights per case; do
not claim resident-model reuse.
CPU reductions report measured errors and seed variability. Refinement inherits
baseline seeds, verifies source manifest and revision identity, and supports an
empty selection without fabricating inference. Keep public handoffs on S3.
See `docs/workbench/alpamayo2-super.md#ray-experiments` for controls and outputs.

Inference now snapshots and validates its manifest before fetching the model,
verifies sidecar sample/seed/projection identity, and fully decodes the PNG before
publication. The upstream sidecar contains metadata and metrics, not full XYZ
coordinates; do not describe it as a coordinate recording.

```bash
npa/.venv/bin/python ~/.codex/skills/.system/skill-creator/scripts/quick_validate.py \
  skills/tools/alpamayo2-super
npa/.venv/bin/python -m pytest \
  npa/tests/workbench/test_alpamayo2_super.py \
  npa/tests/workbench/test_alpamayo_ray_sweep.py \
  npa/tests/workbench/test_alpamayo2_super_service_security.py \
  npa/tests/guardrails/test_skills_index.py \
  npa/tests/orchestration/npa_workflow/test_catalog_doc_sync.py -q
```