---
product_id: 125589101
title: "USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers"
brand: "google coral"
price: "44114 som"
currency: KGS
in_stock: true
reviews_count: 13
category: "Google Coral"
url: https://www.desertcart.kg/products/125589101-usb-edge-tpu-ml-accelerator-coprocessor-for-raspberry-pi-other
store_origin: KG
region: Kyrgyzstan
---

# 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:** 44114 som
**Availability:** ✅ In Stock

## Summary

> 🚀 Supercharge your AI edge with Google’s USB ML powerhouse!

## 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?** 44114 som with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.kg](https://www.desertcart.kg/products/125589101-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

- • **Compact & Linux-Ready:** Tiny footprint with Debian Linux support, perfect for embedded AI projects and home labs.
- • **Blazing Fast ML Inferencing:** Run MobileNet v2 at 100+ frames per second for real-time AI insights.
- • **Seamless USB 3.1 Connectivity:** Plug-and-play with SuperSpeed 5Gb/s USB Type-C for ultra-low latency data transfer.
- • **Broad TensorFlow Compatibility:** Supports TensorFlow Lite models and popular architectures like MobileNet and Inception out-of-the-box.
- • **Power-Efficient AI Acceleration:** Offload heavy ML tasks with minimal power draw, keeping your system cool and efficient.

## Overview

The Google Coral USB Edge TPU Accelerator is a compact, high-performance ML coprocessor designed to boost machine learning inferencing on Linux systems via USB 3.1. Featuring a custom Google Edge TPU ASIC, it delivers 100+ FPS on mobile vision models like MobileNet v2 while maintaining low power consumption. Compatible with TensorFlow Lite and Google Cloud, it supports popular AI architectures and is ideal for embedded AI, home labs, and real-time object detection setups.

## 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: Soild for home lab and AI GPU on the cheap - I’ve been running the Google Coral USB Accelerator as part of my self-hosted Home Assistant and Frigate setup in my home lab, and it’s been a solid upgrade. My cameras stream through it for real-time object detection, and while the AI recognition isn’t perfect, it’s definitely good enough for home security and smart automation triggers. It picks up people, cars, and even the occasional animal (cats 🐱) with decent accuracy, and it’s responsive enough for live notifications or actions. The biggest win is offloading the CPU. Before Coral, my server was getting hammered by the detection workload, especially with multiple cameras running. Now, it’s smooth, CPU usage is way down, and the system feels a lot more stable and responsive. If you’re running Frigate or anything TensorFlow-based in a home setup, the Coral USB is a no-brainer. It’s compact, plug-and-play with a bit of config, and does exactly what it’s meant to. Note that the unit get pretty hot while working, this is normal.
Review: Works great in frigate and significantly reduces CPU usage - Purchased this device from this seller after a previous order from a different seller arrived DOA. Although slightly more expensive, device arrived quickly and haven't had any issues. Using it with Frigate running in a VM on a NUC 12 Pro with 4 cameras. Device works great and performs as promised, reducing CPU usage in the NUC significantly. Would highly recommend it for this purpose. Getting the device flashed, configured, and passing through to the VM is a little tricky and outside of the scope of this review, but for others who intend to use it that way, search for William Lam's guides on this. They're very detailed, easy to follow, and will get you up and running quickly.

## 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 | #87 in Computer Motherboards #408 in Single Board Computers (Computers & Accessories) |
| Brand | Google Coral |
| Connectivity Technology | USB |
| Customer Reviews | 4.1 4.1 out of 5 stars (482) |
| Item Dimensions L x W x H | 3"L x 2"W x 1"H |
| Manufacturer | Google Coral |
| Memory Storage Capacity | 16 KB |
| Mfr Part Number | Coral-USB-Accelerator |
| Model Number | Coral-USB-Accelerator |
| Operating System | Linux |
| Processor Brand | ARM |
| Processor Count | 1 |
| UPC | 608614201389 |

## Product Details

- **Brand:** Google Coral
- **CPU Manufacturer:** ARM
- **Connectivity Technology:** USB
- **Memory Storage Capacity:** 16 KB
- **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

### ⭐⭐⭐⭐⭐ Soild for home lab and AI GPU on the cheap
*by Y***V on April 12, 2025*

I’ve been running the Google Coral USB Accelerator as part of my self-hosted Home Assistant and Frigate setup in my home lab, and it’s been a solid upgrade. My cameras stream through it for real-time object detection, and while the AI recognition isn’t perfect, it’s definitely good enough for home security and smart automation triggers. It picks up people, cars, and even the occasional animal (cats 🐱) with decent accuracy, and it’s responsive enough for live notifications or actions. The biggest win is offloading the CPU. Before Coral, my server was getting hammered by the detection workload, especially with multiple cameras running. Now, it’s smooth, CPU usage is way down, and the system feels a lot more stable and responsive. If you’re running Frigate or anything TensorFlow-based in a home setup, the Coral USB is a no-brainer. It’s compact, plug-and-play with a bit of config, and does exactly what it’s meant to. Note that the unit get pretty hot while working, this is normal.

### ⭐⭐⭐⭐⭐ Works great in frigate and significantly reduces CPU usage
*by R***T on June 26, 2025*

Purchased this device from this seller after a previous order from a different seller arrived DOA. Although slightly more expensive, device arrived quickly and haven't had any issues. Using it with Frigate running in a VM on a NUC 12 Pro with 4 cameras. Device works great and performs as promised, reducing CPU usage in the NUC significantly. Would highly recommend it for this purpose. Getting the device flashed, configured, and passing through to the VM is a little tricky and outside of the scope of this review, but for others who intend to use it that way, search for William Lam's guides on this. They're very detailed, easy to follow, and will get you up and running quickly.

### ⭐ Has lots of potential, but poorly supported and with a mess of non-working examples on Github
*by P***S on October 19, 2023*

I had lots of hope for this... it would have been great to have a self contained TPU solution that can provide an assist to classification and detection tasks which I normally do with OpenCV on either a CISC or GPU right now. In comes Coral. The promise of fast tensor operations using a lower power dongle can't be beat. Now comes the bad: first, good luck finding this for the MSRP. Either production is low, or scalpers are having a field day on this. So, from the get-go you're paying 10-20% premium on the device. Next, if you get your hands on it, good luck trying to get it working with anything. Lots of the reviewers like this because they utilize it with Frigate which is cool. A+ for that workload... Now if you want to use it with anything else, there are some examples... and that's where things hit a bumpy road. Check out any of the Github repositories that are posted. Most were posted 3 or 4 years ago and have been untouched since... so trying bringing down an example and getting it to work... Windows examples don't work... WSL doesn't work... a recent version or LTS on Ubuntu... and same thing... nothing works. Good luck getting a response from the support address. So... summary: this thing is great if you have Frigate and need to increase camera counts without buying more cores or GPUs. It may be great if you can get it running on a RPi and do things that are simply not possible, there... but for CV applications... getting this to run will be the long tent pole due to the poorly maintained examples and stale support repositories making it a better move to just skip over this and go with a GPU solution such as a CUDA accelerated OpenCV approach or Deepstacks or anything else for that matter. If anything changes and I get this thing functional, I'll update this review accordingly.

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*Product available on Desertcart Kyrgyzstan*
*Store origin: KG*
*Last updated: 2026-03-21*