





🚀 Double the TPU, double the AI power—accelerate your edge computing game!
The Seeed Studio Coral M.2 Accelerator features dual Edge TPU coprocessors delivering a combined 8 TOPS of ML inferencing power at just 4 watts total. Designed for seamless integration with Debian Linux and Windows 10 systems via PCIe Gen2 x1, it supports TensorFlow Lite and AutoML Vision Edge for rapid deployment of custom AI models. This compact M.2 module enables ultra-fast, power-efficient on-device machine learning, reducing latency and enhancing data privacy by eliminating cloud reliance.


| ASIN | B0CY231Q61 |
| Best Sellers Rank | #50,116 in Computers ( See Top 100 in Computers ) #637 in Single-Board Computers |
| Item model number | Coral M.2 |
| Manufacturer | seeed studio |
| Package Dimensions | 8.71 x 5.89 x 0.71 cm; 18 g |
T**I
not working at all
E**A
Viene sin caja, metido en una bolsa de plástico de burbujas, y esta a su vez en otra. Un producto muy pequeño, delicado y sin caja del fabricante. Parece de segunda mano o de baja calidad. Aún no la he instalado. Espero funcione. Muy mala presentación para ser tan cara.
W**T
I use this with the free/open source Frigate NVR software that is connected to my Home Assistant. I have an HP EliteDesk 705 that I took out the wifi/bluetooth card because I use a hardwired LAN connection and put this Coral in instead. Sadly it only detects one of the TPUs, but that's not the Coral's fault, it's a limitation on my computer.
S**O
This Coral M.2 Accelerator works flawlessly. I added it to my server specifically for AI object detection with Frigate, and the performance jump is incredible. The dual Edge TPU handles real-time detection smoothly, runs cool, and stays completely stable. Setup was quick, and it integrated with my system without any issues. If you’re using Frigate or need solid hardware acceleration for machine learning tasks, this is absolutely worth it.
J**G
Shoved this in my qnap to speed up the QuMagie AI. Works great shows up as 2 TPU's but had to buy a special multiplexing adapter to an m.2 slot and then also get a m.2 to pci slot card. But that's as much to do with the qnap as the card itself.
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