On-device performance testing for edge AI.

Optimize and benchmark AI on remote edge devices.

Get started Documentation

hub.embedl.com

Fine-tuning results

Terminal

user@computer:~$ $

end-to-end toolbox

store data and artifacts

Supported hardware platforms

No fluff. Just edge AI tools that work.

Explore the full workflow.

1

Python Library

Optimize your model

Compile and quantize your model for efficient on-device inference.

2

Device Cloud

Test on real hardware

Run your model on real edge devices in the cloud.

See supported devices

3

Platform

Analyze & compare

Visualize the performance of your model with our dashboards.

Optimize it. Compile it. Run it.

Deploy your model for any edge device with the Hub Python library.

pick and choose components from our toolbox

Smaller faster models.

Optimize your model for lower latency and memory usage.

user@computer:~$ $

Target every chip.

Compile your model for execution on CPU, GPU, NPU or other AI accelerators on your target devices.

user@computer:~$ $ embedl-hub compile \
-m my_quantized_model.onnx \
--device "Samsung Galaxy S25" \
--runtime tflite

Run your own benchmarks.

Measure latency and memory usage of your model on a real edge device in the cloud.

user@computer:~$ $ embedl-hub profile \
-m my_model.tflite \
--device "Samsung Galaxy S25"

Test your model on remote devices.

Run benchmarks and identify issues early in development.

See supported devices

some examples of hardware you don't have to invest in

Snapdragon 8 Elite (SM8750)

Samsung Galaxy S25Samsung Galaxy S25 UltraSnapdragon 8 Elite QRD

8 Elite

Tensor G4 (GS401)

Google Pixel 9Google Pixel 9 ProGoogle Pixel 9 Pro XL

G4

Exynos 1330 (S5E8535)

Samsung Galaxy A14 5G

1330

Snapdragon 8 Gen 3 (SM8650)

Samsung Galaxy S24Samsung Galaxy S24+Samsung Galaxy S24 Ultra

8 Gen 3

Tensor G3 (GS301)

Google Pixel 8Google Pixel 8 Pro

G3

Exynos 1280 (S5E8825)

Samsung Galaxy A53 5G

1280

Snapdragon 8 Gen 2 (SM8550)

Samsung Galaxy S243Samsung Galaxy S23+Samsung Galaxy S23 Ultra

8 Gen 2

Snapdragon 8 Gen 1 (SM8450)

Samsung Galaxy S22 Ultra 5GXiaomi 12 ProSamsung Galaxy Tab S8

8 Gen 1

Snapdragon 888 (SM8350)

Samsung Galaxy S21Samsung Galaxy S21+Samsung Galaxy S21 Ultra

888

See all devices

All your results in one place.

Analyze and visualize your experiments on the web.

store data and artifacts

get an overview of all KPI:s

hub.embedl.com

Fine-tuning results

hub.embedl.com

Fine-tuning results

Start building for free.

The complete edge AI workflow that works.

Latest blogposts

Quantization - Diagnosing layer sensitivity during post training quantization

Daniel Ödman

10 October 2025

Qualcomm's latest flagship is here - and we're on board

Elina Norling

8 September 2025

9 reasons why we think Edge development is so hard

Elina Norling

Humbled to be a part of

Frequently asked questions

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 780681, No 957197 and No 190109922.