---
product_id: 462766510
title: "USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers"
brand: "google coral"
price: "€ 193.04"
currency: EUR
in_stock: true
reviews_count: 13
category: "Google Coral"
url: https://www.desertcart.be/products/462766510-usb-edge-tpu-ml-accelerator-coprocessor-for-raspberry-pi-other
store_origin: BE
region: Belgium
---

# 100+ fps inferencing speed USB 3.1 Gen 1 SuperSpeed 5Gb/s Google Edge TPU ML coprocessor USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers

**Brand:** google coral
**Price:** € 193.04
**Availability:** ✅ In Stock

## Summary

> 🚀 Supercharge your Raspberry Pi with instant AI power — don’t get left behind!

## Quick Answers

- **What is this?** USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers by google coral
- **How much does it cost?** € 193.04 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.be](https://www.desertcart.be/products/462766510-usb-edge-tpu-ml-accelerator-coprocessor-for-raspberry-pi-other)

## Best For

- google coral enthusiasts

## Why This Product

- Trusted google coral brand quality
- Free international shipping included
- Worldwide delivery with tracking
- 15-day hassle-free returns

## Key Features

- • **Power-Efficient AI Boost:** Offloads CPU with minimal power draw—keep your Pi cool and quiet.
- • **Blazing Fast ML Inferencing:** Run MobileNet v2 at 100+ fps for real-time AI insights.
- • **Seamless USB 3.1 Connectivity:** SuperSpeed 5Gb/s USB Type-C plug & play for instant setup.
- • **Privacy-First Local Processing:** All AI happens on-device—no cloud, no data leaks, total control.
- • **TensorFlow-Ready & Cloud Compatible:** Supports TensorFlow Lite models and Google Cloud integration for future-proof AI.

## Overview

The Coral USB Edge TPU Accelerator is a compact, low-power USB 3.1 device featuring Google's custom Edge TPU coprocessor. It delivers blazing-fast machine learning inferencing (100+ fps) on embedded Linux systems like Raspberry Pi, offloading AI workloads from the CPU. Compatible with TensorFlow Lite and Google Cloud, it supports popular vision models such as MobileNet and Inception, enabling real-time, privacy-preserving AI applications with minimal setup.

## Description

Coral USB Accelerator brings powerful ML (machine learning) inferencing capabilities to existing Linux systems. Featuring the Edge TPU, a small ASIC designed and built by Google, the USB Accelerator provides high performance ML inferencing with a low power cost over a USB 3.0 interface. For example, it can execute state-of-the-art mobile vision models, such as MobileNet v2 at 100+ fps, in a power-efficient manner. This allows fast ML inferencing to embedded AI devices in a power-efficient and privacy-preserving way. Models are developed in TensorFlow Lite and then compiled to run on the USB Accelerator. Edge TPU key benefits: High speed TensorFlow Lite inferencing Low power Small footprint Features Google Edge TPU ML accelerator coprocessor USB 3.0 Type-C socket Supports Debian Linux on host CPU Models are built using TensorFlow. Fully supports MobileNet and Inception architectures though custom architectures are possible Compatible with Google Cloud Specifications Arm 32-bit Cortex-M0+ Microprocessor (MCU): Up to 32 MHz max 16 KB Flash memory with ECC 2 KB RAM Connections: USB 3.1 (gen 1) port and cable (SuperSpeed, 5Gb/s transfer speed) Included cable is USB Type-C to Type-A Coral, a division of Google, helps build intelligent ideas with a platform for local AI.

Review: The "Holy Grail" for local Home Assistant AI detection! - The Bottom Line: If you're running Frigate or any local NVR software on a Raspberry Pi, stop using your CPU for detection and buy this. It transforms slow, laggy "motion" alerts into near-instant "person" or "car" notifications. The Game Changer: Instant Detection: Before this, my Raspberry Pi struggled to keep up with camera streams. Now, object detection is lightning-fast (usually under 10ms inference time). CPU Lifesaver: It offloads all the heavy lifting from the Pi’s processor. My CPU usage dropped from 60–80% down to a cool 10–15% because the TPU handles the AI. Low Power, High Gain: For a device that adds this much "brainpower," it draws very little current. It runs perfectly fine off the Pi’s USB 3.0 port without needing an external power supply in my setup. Privacy First: I love that all my camera analysis happens locally in my house—nothing is being sent to a cloud server in another country. Pro-Tips for Setup: Use USB 3.0: Make sure you plug it into the blue USB ports on the Pi 4 or 5. It needs that bandwidth to perform at its peak. Heat: It can get a little warm during heavy use, so make sure your Pi case has decent airflow. Home Assistant: It’s basically "plug and play" once you add the Coral drivers to your config. If you aren't using Frigate with this yet, you're missing out! The Verdict: It’s getting harder to find these in stock, so if you see one, grab it. It is the single best upgrade you can make for a smart home security system.
Review: An exceptional piece of equipment - This is a powerful device. I currently have 5 cameras running inference @ 4Hz and I'm using 12-17% of it's capacity. Be aware that you are unlikely to get it to run on Windows, it needs Linux. You will also encounter a lot of software version issues, so be prepared to put a fair bit of time into developing for it. It's worth it though, this thing really delivers!

## Features

- Specifications: Arm 32-bit Cortex-M0+ microprocessor (MCU): up to 32 MHz max 16 KB flash memory with ECC 2 KB RAM connections: USB 3.1 (Gen 1) port and cable (SuperSpeed, 5Gb/s transfer speed)
- Features: Google Edge TPU ML acceleration coprocessor, USB 3.0 Type-C female, supports Debian Linux to host CPU, models are built with TensorFlow Supports MobileNet and Inception architectures through custom architectures are possible. Compatible with Google Cloud
- Specifications: Arm 32-bit Cortex-M0+ Microprocessor (MCU): Up to 32 MHz max 16 KB Flash memory with ECC 2 KB RAM Connections: USB 3.1 (gen 1) port and cable (SuperSpeed, 5Gb/s transfer speed)
- Features: Google Edge TPU ML accelerator coprocessor, USB 3.0 Type-C socket, Supports Debian Linux on host CPU, Models are built using TensorFlow. Fully supports MobileNet and Inception architectures through custom architectures are possible. Compatible with Google Cloud.
- Features: Google Edge TPU ML accelerator coprocessor, USB 3.0 Type-C socket, Supports Debian Linux on host CPU, Models are built using TensorFlow. Full supports MobileNet and Inception architectures through custom architectures are possible. Compatible with Google Cloud.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| ASIN | B07R53D12W |
| Best Sellers Rank | 6,352 in Computers & Accessories ( See Top 100 in Computers & Accessories ) 53 in Single-Board Computers & Accessories |
| Brand | Google Coral |
| Brand Name | Google Coral |
| CPU manufacturer | ARM |
| Connectivity technology | USB |
| Country of Origin | USA |
| Customer Reviews | 4.2 out of 5 stars 500 Reviews |
| Item Dimensions L x W x H | 7.6L x 5.1W x 2.5H centimetres |
| Manufacturer | Google Coral |
| Manufacturer Part Number | Coral-USB-Accelerator |
| Memory Storage Capacity | 16 KB |
| Memory storage capacity | 16 KB |
| Model Name | Coral-USB-Accelerator |
| Model Number | Coral-USB-Accelerator |
| Model name | Coral-USB-Accelerator |
| Network Connectivity Technology | USB |
| Operating System | Linux |
| Operating system | Linux |
| Processor Brand | ARM |
| Processor Count | 1 |
| Total USB Ports | 1 |
| UPC | 608614201389 |

## Product Details

- **Brand:** Google Coral
- **Connectivity technology:** USB
- **Memory storage capacity:** 16 KB
- **Model name:** Coral-USB-Accelerator
- **Operating system:** Linux

## Images

![USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers - Image 1](https://m.media-amazon.com/images/I/61J05USFjaL.jpg)
![USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers - Image 2](https://m.media-amazon.com/images/I/51o1kWSC2VL.jpg)
![USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers - Image 3](https://m.media-amazon.com/images/I/519RoZ2Bi5L.jpg)
![USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers - Image 4](https://m.media-amazon.com/images/I/41Pj5MCr3gL.jpg)

## Questions & Answers

**Q: could this be used to dramatically speed up Hashcat?**
A: No, Hashcat don't support the TPU and the TPU wouldn't support hashcat, in theory. The TPU is an ASIC that doesn't even do the right kind of math for password hashing. It's built for Matrix operations for interference training, not low precision parallel integer operations.

**Q: Is this only the USB accelerator or the development board?**
A: This is the USB accelerator

**Q: Can it run on Mac?**
A: The Coral USB Accelerator adds a Coral Edge TPU to your Linux, Mac, or Windows computer.

**Q: I thought the list price was 59.99, what is up with the scalping?**
A: Demand vs supply

## Customer Reviews

### ⭐⭐⭐⭐⭐ The "Holy Grail" for local Home Assistant AI detection!
*by S***U on 11 February 2026*

The Bottom Line: If you're running Frigate or any local NVR software on a Raspberry Pi, stop using your CPU for detection and buy this. It transforms slow, laggy "motion" alerts into near-instant "person" or "car" notifications. The Game Changer: Instant Detection: Before this, my Raspberry Pi struggled to keep up with camera streams. Now, object detection is lightning-fast (usually under 10ms inference time). CPU Lifesaver: It offloads all the heavy lifting from the Pi’s processor. My CPU usage dropped from 60–80% down to a cool 10–15% because the TPU handles the AI. Low Power, High Gain: For a device that adds this much "brainpower," it draws very little current. It runs perfectly fine off the Pi’s USB 3.0 port without needing an external power supply in my setup. Privacy First: I love that all my camera analysis happens locally in my house—nothing is being sent to a cloud server in another country. Pro-Tips for Setup: Use USB 3.0: Make sure you plug it into the blue USB ports on the Pi 4 or 5. It needs that bandwidth to perform at its peak. Heat: It can get a little warm during heavy use, so make sure your Pi case has decent airflow. Home Assistant: It’s basically "plug and play" once you add the Coral drivers to your config. If you aren't using Frigate with this yet, you're missing out! The Verdict: It’s getting harder to find these in stock, so if you see one, grab it. It is the single best upgrade you can make for a smart home security system.

### ⭐⭐⭐⭐⭐ An exceptional piece of equipment
*by J***O on 7 April 2026*

This is a powerful device. I currently have 5 cameras running inference @ 4Hz and I'm using 12-17% of it's capacity. Be aware that you are unlikely to get it to run on Windows, it needs Linux. You will also encounter a lot of software version issues, so be prepared to put a fair bit of time into developing for it. It's worth it though, this thing really delivers!

### ⭐⭐⭐⭐ Works well but applications seem limited
*by A***R on 10 July 2024*

Like 99% of other reviewers, I used the Coral TPU USB with Frigate to offload object inference from the CPU. This it does very well. Amazing that such a small, low cost device can do this but it goes to show how purpose built hardware can be remarkably efficicient at a specific task. I only have a couple of cameras at the moment and the device does not even get warm. Inference speed is slightly disappointing (30ms), but I put this down to the older PC it is running on. EDIT: Switching from USB2 to USB3 port brought inference speed to 8.5ms) Now for the negatives. The device changes USB ID once initialised. This can make virtualisation more difficult or less secure. There seem to be very few applications that make drop-in use of these coral devices. The device is sold as a devloper board, so possibly risks becoming another Google abandon-ware project. Still, it was quite an eye-opener to see what this little device can achieve when compared to the cpu grunt required to do the same. Currently at 65 quid, it's a far more economical proposition than it was a year ago.

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*Product available on Desertcart Belgium*
*Store origin: BE*
*Last updated: 2026-05-23*