Google
Coral Dev Board Mini
The Coral Dev Board Mini is a compact single-board computer engineered for efficient on-device machine learning. It integrates Google's Edge TPU coprocessor, delivering rapid AI inferencing capabilities within a small footprint. Running Mendel Linux, a Debian derivative, it provides a flexible development environment for embedded ML projects. This board is optimized for TensorFlow Lite models and supports AutoML Vision Edge for custom image classification tasks. Enhanced with built-in Wi-Fi 5 and Bluetooth 5.0, it offers robust connectivity for diverse applications.
$99.99
Owner Satisfaction
4.4
/ 5
Category Rank
1007
/ 1543
#1007 in Development Boards
Price vs Category Average
+14%
Above average
CPU
816735
/ MediaTek s SoC (Quad-core Arm Cortex-A)
Who it's for
- High-Speed Edge TPU Acceleration
- Compact Form Factor and Rich I/O
- Seamless TensorFlow Lite Integration
Who should skip it
- Inference-Only Hardware
- Specific Model Optimization Required
- Limited Availability and Ecosystem Uncertainty
Key Specs
CPU
MediaTek 8167s SoC (Quad-core Arm Cortex-A35)
ML Accelerator
Google Edge TPU coprocessor (4 TOPS)
RAM
2 GB LPDDR3
Storage
8 GB eMMC onboard flash
Wireless
Wi-Fi 5 (802.11a/b/g/n/ac) and Bluetooth 5.0
I/O
40-pin GPIO header
Connectivity
2x USB Type-C (USB 2.0)
Display Output
Micro HDMI (1.4)
Features
- Fast ML inferencing with Edge TPU
- Complete embedded system with SoC
- Mendel Linux for development flexibility
- Supports TensorFlow Lite models
- Built-in Wi-Fi 5 and Bluetooth 5.0
- Compact form factor
- Camera and display interfaces
- Onboard digital microphone
What customers say
Developers highly value the Coral Dev Board Mini for its specialized edge machine learning capabilities. The integrated Edge TPU earns praise for delivering exceptional, low-latency inference efficiency when handling complex AI workloads like object detection. Users appreciate the compact design and built-in connectivity, making it perfect for small, deployable IoT solutions. While the initial setup requires familiarity with specific frameworks, the consensus is that the high quality of the dedicated hardware justifies the cost for those needing powerful, low-power AI acceleration. It is strongly rated within its niche for cutting-edge performance.
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