File size: 3,562 Bytes
49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed 3ef37e9 49b0fed 8e64da1 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed 8e64da1 49b0fed bd2ad48 49b0fed bd2ad48 49b0fed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | ---
license: other
license_name: nvidia-license
license_link: https://huggingface.co/nvidia/Alpamayo-1.5-10B/blob/main/LICENSE
pipeline_tag: robotics
language:
- en
inference: false
base_model:
- nvidia/Alpamayo-R1-10B
new_version: z-lab/Alpamayo-1.5-10B
datasets:
- nvidia/PhysicalAI-Autonomous-Vehicles
- nvidia/PhysicalAI-Autonomous-Vehicles-NuRec
tags:
- flashdrive
- autonomous-driving
- vision-language-action
- alpamayo
---
# Alpamayo 1 (R1) (FlashDrive)
**Flash Vision-Language-Action Inference for Autonomous Driving**
[](https://github.com/z-lab/flashdrive)
[](https://z-lab.ai/projects/flashdrive/)
[](https://huggingface.co/collections/z-lab/flashdrive)
[FlashDrive](https://github.com/z-lab/flashdrive) accelerates [Alpamayo 1 (R1)](https://huggingface.co/nvidia/Alpamayo-R1-10B) — 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](https://github.com/z-lab/dflash) speculative reasoning, [ParoQuant](https://github.com/z-lab/paroquant) W4A8 quantization, adaptive action caching, and `torch.compile`.
This repository mirrors the weights of [nvidia/Alpamayo-R1-10B](https://huggingface.co/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](https://huggingface.co/z-lab/Alpamayo-R1-10B-PARO) | W4A8 ([ParoQuant](https://github.com/z-lab/paroquant)) language-model weights |
| [z-lab/Alpamayo-R1-10B-DFlash](https://huggingface.co/z-lab/Alpamayo-R1-10B-DFlash) | [DFlash](https://github.com/z-lab/dflash) block-diffusion draft model |
## Usage
Install [FlashDrive](https://github.com/z-lab/flashdrive), then load this base checkpoint — the `-PARO` and `-DFlash` companions are fetched automatically:
```python
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:
```bash
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](https://github.com/z-lab/flashdrive#performance) for the full benchmark.
## License
The Alpamayo weights in this repository are released by NVIDIA under the [NVIDIA License](https://huggingface.co/nvidia/Alpamayo-1.5-10B/blob/main/LICENSE), which permits **non-commercial use only** and extends to derivative works. The [FlashDrive](https://github.com/z-lab/flashdrive) inference code is separately released under the [MIT License](https://github.com/z-lab/flashdrive/blob/main/LICENSE).
## Citation
```bibtex
@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}
}
```
|