EagleVLA-GGUF

EagleVLA VLA GGUF Models

GGUF conversions of PI0, PI0.5, and NVIDIA Isaac GR00T N1.7 for deployment with EagleVLA-Edge, a llama.cpp-based inference engine for real-time vision-language-action models on NVIDIA Jetson and other CUDA-capable platforms.

Model files

Always use the main model and mmproj from the same model directory.

Download

huggingface-cli download diantoudefengshan/EagleVLA-GGUF \
  gr00t-n1d7/gr00t-n1d7-bf16.gguf \
  gr00t-n1d7/mmproj-gr00t-n1d7-bf16.gguf \
  --local-dir EagleVLA-GGUF

Runtime

Build EagleVLA-Edge with CUDA support:

git clone https://github.com/PKU-SEC-Lab/EagleVLA-Edge.git
cd EagleVLA-Edge
cmake -S . -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --target llama-server jetson_pi_gr00t -j

Start GR00T server mode (do not set PI_MODEL):

./build/bin/llama-server \
  -m /path/to/gr00t-n1d7/gr00t-n1d7-bf16.gguf \
  --mmproj /path/to/gr00t-n1d7/mmproj-gr00t-n1d7-bf16.gguf \
  -ngl 99 --host 0.0.0.0 --port 8080

PI0 and PI0.5 server commands and the full foreground HTTP API are documented in the EagleVLA-Edge README.

GR00T conversion and validation

The GR00T files were converted from nvidia/GR00T-N1.7-3B and nvidia/Cosmos-Reason2-2B with the GR00T converter in EagleVLA-Edge. The main GGUF contains 715 tensors and the mmproj contains 316 tensors. They passed the repository's metadata, tensor-name/shape, 16-backbone-block, vision-split, and end-to-end action checks. With aligned inputs and initial denoising noise, the EagleVLA normalized [40, 132] action achieved cosine similarity 0.999968648 against the official NVIDIA PyTorch implementation.

SHA-256:

8cd6af6b33d90d9b6b6fdbf419dbdbc263393dbcb9ad71bbd226889b4285d573  gr00t-n1d7-bf16.gguf
f9c442143857ee01bf95192516de8169af7e0e136d410942a3eeee316c06bdd4  mmproj-gr00t-n1d7-bf16.gguf

Intended use and license

These models are intended for research on local and onboard VLA inference, robot-control systems, deployment optimization, and reproducibility. They are not general-purpose chat models. The converted weights remain subject to their respective upstream model licenses and terms; review those terms before redistribution or deployment. EagleVLA-Edge source code is distributed under its repository license.

Acknowledgments

This work builds on OpenPI, NVIDIA Isaac-GR00T, Cosmos-Reason2, and llama.cpp.

Citation

@article{yang2026eaglevla,
  title={EagleVLA: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference},
  author={Yang, Zebin and Wang, Qi and Wang, Yunhe and Guo, Xiurui and Yu, Bo and Liu, Shaoshan and Xu, Jiafeng and Dong, Hao and Li, Meng},
  journal={arXiv preprint arXiv:2607.12659},
  year={2026}
}