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Google Coral Dev Board vs NVIDIA Jetson Nano

Side-by-side comparison of features, pricing, ratings, and alternatives.

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Google Coral Dev Board
Google Coral Dev BoardA single-board computer with a removable system-on-module for edge AI applications.
NVIDIA Jetson Nano
NVIDIA Jetson NanoA small, powerful AI computer for makers, learners, and embedded developers.
Overview
Description

The Google Coral Dev Board is a powerful single-board computer designed for rapid prototyping of low-power edge AI devices. It features Google's proprietary Edge TPU coprocessor, capable of performing 4 trillion operations per second while consuming very little energy. Equipped with a complete system containing NXP i.MX 8M SoC, LPDDR4 RAM, and onboard wireless connectivity, the board runs a specialized Debian-based Linux system called Mendel. It provides developers with a complete, out-of-the-box environment to build and deploy high-performance machine learning models locally at the edge.

The NVIDIA Jetson Nano Developer Kit delivers the compute performance to run modern AI workloads in a small, power-efficient form factor. It brings the power of modern artificial intelligence to edge devices, empowering developers to build autonomous machines and smart devices. Equipped with a 128-core Maxwell GPU and a quad-core ARM CPU, it supports high-resolution sensors and processes multiple neural networks concurrently. Backed by the comprehensive Jetson software stack, it streamlines the development and deployment of advanced robotics and IoT applications.

Pricing
Paid (One-time)
Paid (One-time)
Category
Development Boards
ARM Single-Board Computers
Best for
Embedded AI engineers, developers, and researchers
Hobbyists, students, and embedded AI developers
Specifications
General
msrp
$149.99
$99โˆ’34%
Specifications
weight
250g+79%
140g
warranty
1 year
1 year
dimensions
88 mm x 60 mm
100 mm x 80 mm x 29 mm+14%
open source
No
No
connectivity
Gigabit Ethernet, Wi-Fi 2x2 MIMO 802.11b/g/n/ac, Bluetooth 4.2
Gigabit Ethernet, M.2 Key E (for wireless), USB 3.0, USB 2.0
power source
USB Powered
AC Powered
Pros & Cons
Pros
  • High-speed local inferencing via Edge TPU
  • Removable SOM simplifies custom board design
  • Full Linux development environment
  • Low power consumption during heavy AI workloads
  • Affordable entry point for edge AI development
  • Comprehensive software stack via JetPack SDK
  • Compact form factor with versatile I/O interfaces
  • Active developer community with extensive tutorials
Cons
  • Can run quite warm under sustained heavy loads
  • Limited to TensorFlow Lite models
  • Discontinued or supply-constrained availability in recent years
  • Limited onboard memory compared to higher-end Jetson models
  • Discontinued official production affecting long-term availability
  • Power supply requirements can be strict under heavy loads
Community & Metrics
Upvotes
0
0
User rating
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