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Adoption notes

The engine works; the gap is distribution.

Done:

  1. C ABI. include/vla.h and libvla. src/model.h is C++ only, so without it nothing outside C++ can link the engine.
  2. Python bindings. bindings/python, ctypes over the ABI. pip install ./bindings/python builds a self-contained wheel with scikit-build-core.
  3. Prebuilt binaries. .github/workflows/release.yml publishes on tag: linux-x86_64 (CPU, CUDA 12.8, CUDA 13.4), linux-aarch64 (CPU, and CUDA 13.4 for Orin, Thor and DGX Spark), macos-arm64-metal, and a Docker image. The tarballs now run off the build machine: shared libraries ship next to the binaries with an $ORIGIN rpath, builds use GGML_NATIVE=OFF, and CI runs vla-cli --help with the build tree moved away. vla-server still needs libzmq5 from the system.
  4. Install rules. cmake --install gives a relocatable tree with a $ORIGIN/../lib rpath.
  5. One-command model fetch. -hf user/repo[:path/file.gguf|:tag] on vla-cli, vla-server and vla-bench, cached under $VLA_CACHE. A repo with several GGUFs lists them instead of guessing.
  6. Reproducible benchmarks. vla-bench emits the README table rows.
  7. Contributor path. CONTRIBUTING.md has the six-site walkthrough for adding an architecture, plus issue and PR templates.
  8. Instruction in, action out. vla-cli --text builds each arch's real prompt. Octo carries its tokenizer in the GGUF, and scripts/add_tokenizer_to_gguf.py adds one to pi0, pi0.5 and OpenVLA-OFT, so those need no Python at run time. The other archs call scripts/tokenize_prompt.py.

Left:

  • Jetson on JetPack 6. The tarballs are built on Ubuntu 24.04 and its glibc, and the aarch64 CUDA one needs a CUDA 13 driver, so JetPack 6 Orins still build on the device.
  • PyPI. The wheel builds from bindings/python, but nothing publishes it.
  • Hugging Face library registration. The vrfai model cards set library_name: vla.cpp, but vla.cpp is not registered in huggingface.js, so the Hub shows no "Use this model" snippet.
  • Windows zips and a Homebrew formula. Windows builds from source only, and there is no brew tap, though the install rules now make one possible.
  • ci/baselines/rtx3090.json still disagrees with the README table, which is now RTX 5090 numbers from vla-bench. Re-record the baselines on one machine.
  • Success rates. The README table comes from a May 2026 RTX 3060 sweep and covers seven of the thirteen archs, and π0, SmolVLA and GR00T N1.7 have moved since. A fresh sweep would cover the rest.

None of these change inference behaviour.