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---
title: README
emoji: 🚀
colorFrom: indigo
colorTo: yellow
sdk: static
pinned: true
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/6634fc18d94421fe1c02f97c/48breLiEtms1xr-xl36dc.png
short_description: Embedl - efficient AI for the edge
---
# Embedl
Embedl develops advanced tools and algorithms for **Edge AI**. Our mission is to make AI models run
**faster**, **more energy-efficient**, and **reliably across diverse hardware platforms**, while
significantly reducing development time.
We help teams deploy high-performance AI on real-world, resource-constrained devices.
### **Embedl Models** ([Community](https://github.com/embedl/embedl-models))
Pre-optimized models that can be used **off-the-shelf** or customized for specific hardware target
supported by the [embedl-models](https://github.com/embedl/embedl-models) package.
**First release highlights:**
- The **fastest Small Language Models (SLMs)** using **[FlashHead](https://www.embedl.com/knowledge/ultra-efficient-llms-embedls-breakthrough-for-on-device-ai)**,
a novel architectural improvement to the language-model head
- Works with popular models like **Llama, Gemma, and Qwen**
- Provides speedups on top of:
- Quantization
- Flash Attention
- Other standard optimizations
Device: Nvidia Jetson Thor
| Model | Generation speed (tokens/s) |
| ------------------------------------------------ | ----------------------------|
| embedl/Llama-3.2-3B-Instruct-FlashHead-W4A16 | 100 |
| Llama-3.2-3B-Instruct-W4A16* | 80 |
| RedHatAI/Llama-3.2-3B-Instruct-FP8 | 64 |
| meta-llama/Llama-3.2-3B-Instruct | 37 |
*Embedl quantized model for benchmarking similar to the FlashHead-W4A16 but without
the faster FlashHead and custom generation loop.
---
## Contact
**Headquarters (Sweden)**
Gamla Almedalsvägen 39
412 63 Gothenburg, Sweden
**Email:** info@embedl.com |