Alpamayo 1 (R1) (FlashDrive)
Flash Vision-Language-Action Inference for Autonomous Driving
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:
| Checkpoint | Contents |
|---|---|
| z-lab/Alpamayo-R1-10B-PARO | W4A8 (ParoQuant) language-model weights |
| z-lab/Alpamayo-R1-10B-DFlash | DFlash block-diffusion draft model |
Usage
Install FlashDrive, then load this base checkpoint — the -PARO and -DFlash companions are fetched automatically:
import flashdrive
model = flashdrive.from_pretrained("z-lab/Alpamayo-R1-10B")
pred_xyz, pred_rot = model.sample_trajectories_streaming(data)
The first call per stream only prefills the KV cache and returns (None, None); every later window returns trajectories. For an end-to-end benchmark on a PhysicalAI-AV clip:
python scripts/infer.py --model-path z-lab/Alpamayo-R1-10B
Performance
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.
License
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.
Citation
@article{li2026flashdrive,
title = {{FlashDrive: Flash Vision-Language-Action Inference for Autonomous Driving}},
author = {Li, Zekai and Liang, Yihao and Zhang, Hongfei and Chen, Jian and Liang, Yesheng and Liu, Zhijian},
year = {2026}
}