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:

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 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.

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.

• 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 is the accepted runtime-fetch baseline. Its OCI index digest is sha256:2164450f8baf57d8798f64063ea27bf11611f5b695c467de0c2e319e3134ebd5. On 2026-08-18 the exact digest completed the real upstream workflow on B200 (sm_100) and, independently, RTX PRO 6000 (sm_120). The 26-layer payload scan was clean. RTX peak allocation was 71,447 MiB; the B200 run completed but was not sampled for peak memory, so retain NVIDIA's 72,115 MiB H100 measurement as the conservative documented reference rather than inventing a B200 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

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_alpamayo2_super_service_security.py \
  npa/tests/guardrails/test_skills_index.py \
  npa/tests/orchestration/npa_workflow/test_catalog_doc_sync.py -q