---
license: other
license_name: embedl-software-2.1
license_link: https://embedl.com/legal/software-licence/v2.1
base_model: nvidia/Alpamayo2-Edge
pipeline_tag: robotics
tags:
  - autonomous-driving
  - vision-language-action
  - alpamayo
  - jetson-thor
  - tensorrt-edgellm
  - nvfp4
  - mxfp8
  - embedl
---

# Alpamayo 2 Edge — 92 ms on Jetson AGX Thor

**10.9 decisions/s on NVIDIA Jetson AGX Thor: 6.6× faster than NVIDIA's PyTorch BF16, with lower trajectory error.**

<!-- embedl-run-stickers:start -->
<p class="dark:hidden not-prose" style="margin:8px 0 16px 0;">
  <a href="https://embedl.com/nvidia/alpamayo2-edge/thor?tab=run&amp;utm_source=huggingface&amp;utm_medium=model-card&amp;utm_campaign=run-sticker&amp;utm_content=embedl%2FAlpamayo2-Edge" title="Run Alpamayo 2 Edge on AGX Thor: 92 ms, free on Embedl's device farm, embedl.com"><img src="https://storage.googleapis.com/embedl-model-media/sha256/734cba61c34690a878e4c9e908e767bafd65747b36f371e9e97c98d5d29e00d8.png" alt="Run on AGX Thor: 92 ms. Free on Embedl's device farm (embedl.com)" width="270" height="108" style="display:inline-block;margin:0 2px 4px 0;"></a>
</p>
<p class="hidden dark:block not-prose" style="margin:8px 0 16px 0;">
  <a href="https://embedl.com/nvidia/alpamayo2-edge/thor?tab=run&amp;utm_source=huggingface&amp;utm_medium=model-card&amp;utm_campaign=run-sticker&amp;utm_content=embedl%2FAlpamayo2-Edge" title="Run Alpamayo 2 Edge on AGX Thor: 92 ms, free on Embedl's device farm, embedl.com"><img src="https://storage.googleapis.com/embedl-model-media/sha256/3dd3d0a4af0b47600282a0586cb45a96d610be6c9e3ddf6f596795fc227263d4.png" alt="Run on AGX Thor: 92 ms. Free on Embedl's device farm (embedl.com)" width="270" height="124" style="display:inline-block;margin:0 2px 4px 0;"></a>
</p>
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> **▶ Run Alpamayo 2 Edge live on a real Jetson AGX Thor — free, in your browser:
> [embedl.com/nvidia/alpamayo2-edge/thor](https://embedl.com/nvidia/alpamayo2-edge/thor?tab=run&utm_source=huggingface&utm_medium=model-card&utm_campaign=run-cta&utm_content=embedl%2FAlpamayo2-Edge)**

NVIDIA's [Alpamayo 2 Edge](https://huggingface.co/nvidia/Alpamayo2-Edge), the
vision-language-action model for autonomous driving, quantized and optimized
by Embedl to run in real time on NVIDIA Jetson AGX Thor with TensorRT Edge-LLM,
using 4.2× less energy per decision than NVIDIA's PyTorch BF16 release on the
same device.

<video src="https://storage.googleapis.com/embedl-model-media/sha256/6f0f8172b7b658e95efef45add6f18c5b46ec3dd78d518dcbd627f0b74c3ee34.mp4" controls muted playsinline width="100%"></video>

## Run it live

Try the optimized model on a real Jetson AGX Thor in Embedl's device farm, for
free, with no hardware or setup:
**[Run Alpamayo 2 Edge on AGX Thor →](https://embedl.com/nvidia/alpamayo2-edge/thor?tab=run&utm_source=huggingface&utm_medium=model-card&utm_campaign=run-cta&utm_content=embedl%2FAlpamayo2-Edge)**

## Get the model

The model and its runtime are distributed as container images from Embedl:
see **[Alpamayo 2 Edge on embedl.com](https://embedl.com/nvidia/alpamayo2-edge)**
for the downloads and instructions.

## Quantization

| Component | Precision |
|---|---|
| Language model | NVFP4 (driving-calibrated), FP16 `lm_head` |
| Vision encoder (ViT) | MXFP8 |
| Action expert | MXFP8 |

Built from NVIDIA's released weights, `nvidia/Alpamayo2-Edge` at `b92b6aa3`.
Runtime: TensorRT Edge-LLM 0.10.1 with Embedl optimizations.

## Results on Jetson AGX Thor

| Metric | Embedl NVFP4 / MXFP8 | TensorRT Edge-LLM FP16 | NVIDIA PyTorch BF16 |
|---|---:|---:|---:|
| Latency per decision, median | **91.9 ms** | 489.3 ms | 603.7 ms |
| Latency per decision, p90 | **140.3 ms** | | 840.6 ms |
| Decisions per second | **10.9 Hz** | | 1.7 Hz |
| Energy per decision | **12.45 J** | 62.02 J | 51.87 J |
| Power while deciding, mean | 117.6 W | | 74.5 W |
| Power while deciding, peak | 150.2 W | | 137.9 W |
| Model size | **2.37 GiB** | | 4.95 GiB |
| Runtime container size | **1.07 GiB** | | 11.35 GiB |
| minADE₆ at 5 s (lower is better) | **0.464 m** | | 0.508 m |

Board power at idle: 30.0 W.

### Methodology

- **Latency:** one PhysicalAI-AV clip, all 180 decisions (one per 0.1 s of
  driving), run one after another on the device. A decision is the model's
  whole call from camera frames to trajectory, with the inputs on the GPU.
  Decisions per second is 1000 / the median.
- **Accuracy:** minADE₆ at 5 s, the closest of 6 sampled trajectories,
  average displacement over the first 5 s, on 378 held-out PhysicalAI-AV
  validation clips, 2 seeds. The Embedl build is measured on the device;
  NVIDIA's BF16 on an H200 with NVIDIA's own code, on the same events and
  seeds.
- **Power:** the board's input power (tegrastats, every 500 ms) sampled
  during the 180 timed decisions; energy per decision is the mean power times
  the mean decision time.
- **Size:** the runtime container image and the model files on disk.
- **TensorRT Edge-LLM FP16:** the same release in FP16 on the same runtime and
  device, without Embedl's quantization, caches or speculation.
- **NVIDIA PyTorch BF16:** NVIDIA's released code (NVlabs/alpamayo2-edge) and
  BF16 weights with its `--accel` tuned path, on the same device and inputs.

## License

This optimized model is licensed under the
[Embedl Software Licence 2.1](https://embedl.com/legal/software-licence/v2.1).
It is derived from [nvidia/Alpamayo2-Edge](https://huggingface.co/nvidia/Alpamayo2-Edge);
NVIDIA's original weights remain under their own licence.