FlashDrive accelerates
Alpamayo 1 (R1) — one of NVIDIA's 10B-parameter vision-language-action models for autonomous driving — by
4.5× with no loss in accuracy, through streaming inference,
DFlash speculative reasoning,
ParoQuant W4A8 quantization, adaptive action caching, and
torch.compile.
This repository mirrors the weights of
nvidia/Alpamayo-R1-10B and is the
base checkpoint of the FlashDrive stack. Loading it pulls the derived companions automatically:
Install
FlashDrive, then load this base checkpoint — the
-PARO and
-DFlash companions are fetched automatically:
1import flashdrive
2
3model = flashdrive.from_pretrained("z-lab/Alpamayo-R1-10B")
4
5pred_xyz, pred_rot = model.sample_trajectories_streaming(data)
On a single RTX PRO 6000, averaged over 100 PhysicalAI-AV clips, FlashDrive runs Alpamayo 1 (R1) at
4.5× lower latency (704 → 155 ms per window) while minADE improves from 1.869 to 1.662. See the
repository for the full benchmark.
The Alpamayo weights in this repository are released by NVIDIA under the
NVIDIA License, which permits
non-commercial use only and extends to derivative works. The
FlashDrive inference code is separately released under the
MIT License.
1@article{li2026flashdrive,
2 title = {{FlashDrive: Flash Vision-Language-Action Inference for Autonomous Driving}},
3 author = {Li, Zekai and Liang, Yihao and Zhang, Hongfei and Chen, Jian and Liang, Yesheng and Liu, Zhijian},
4 year = {2026}
5}