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Khadas Edge
The Khadas Edge is a high-performance ARM-based single-board computer featuring a Rockchip RK3399 hexa-core processor, designed for demanding embedded applications, AI inference, and multimedia processing. Professional developers and systems integrators use the Khadas Edge for edge computing deployments, robotics projects, and real-time video processing applications requiring superior computational power compared to traditional SBCs. This board solves the critical problem of delivering desktop-class performance in a compact form factor while maintaining low power consumption for always-on edge computing scenarios.
Product Overview
The Khadas Edge represents a significant advancement in single-board computing, powered by the Rockchip RK3399 processor which integrates dual ARM Cortex-A72 cores clocked at 1.8GHz and quad ARM Cortex-A53 cores at 1.4GHz, complemented by a dedicated Mali-T860 MP4 GPU. This heterogeneous multi-core architecture enables asymmetric processing where computationally intensive tasks leverage the high-performance A72 cores while background tasks utilize the efficient A53 cores, resulting in optimal power efficiency. The board includes 4GB of LPDDR4 RAM and supports up to 64GB eMMC storage, providing ample resources for complex applications including machine learning frameworks, containerized workloads, and real-time video encoding at 4K resolution.
What distinguishes the Khadas Edge from competing platforms is its comprehensive I/O ecosystem and professional-grade thermal management. The board features dual USB 3.0 ports for high-speed data transfer, Gigabit Ethernet for network connectivity, HDMI 2.0 output supporting 4K@60Hz video, and a 40-pin GPIO header compatible with Raspberry Pi peripherals. Advanced cooling through a copper heat spreader and optional active cooling ensures sustained performance under continuous load. The device runs mainline Linux kernels and supports Ubuntu, Debian, and Android operating systems, making it ideal for developers requiring upstream kernel support and long-term software maintenance without vendor lock-in.
Key Specifications
| Specification | Details |
| Product Type | ARM-based Single Board Computer |
| Brand | Khadas |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| Processor | Rockchip RK3399 Hexa-core (2x ARM Cortex-A72 @ 1.8GHz, 4x ARM Cortex-A53 @ 1.4GHz) |
| GPU | Mali-T860 MP4 with OpenGL ES 3.2 support |
| RAM | 4GB LPDDR4 (dual-channel) |
| Storage | 32GB or 64GB eMMC 5.1 with optional microSD card expansion |
| Video Output | HDMI 2.0 (4K@60Hz), MIPI DSI connector for LCD panels |
| Connectivity | Gigabit Ethernet, Dual USB 3.0, USB Type-C, 40-pin GPIO header |
| Power Consumption | 5-15W typical operation, 20W peak |
Key Features
- Rockchip RK3399 Hexa-core Processor delivering 2.4x performance improvement over quad-core alternatives for parallel workload execution
- Mali-T860 MP4 GPU with 4 execution engines enabling hardware-accelerated video encoding/decoding and 3D graphics rendering
- Dual USB 3.0 ports providing 5Gbps bandwidth for high-speed data transfer and external storage connectivity
- Mainline Linux kernel support ensuring long-term software stability and security updates without vendor dependency
- 40-pin GPIO header with PWM, SPI, I2C, and UART interfaces for seamless integration with industrial sensors and actuators
- MIPI DSI and CSI connectors for direct integration with camera modules and LCD displays without USB overhead
- Professional thermal design with copper heat spreader maintaining stable operation under sustained 100% CPU load
- Gigabit Ethernet with optional WiFi/Bluetooth module support for robust network-connected edge deployments
Applications and Use Cases
- Edge AI Inference: Deploy pre-trained TensorFlow Lite and ONNX models for real-time object detection, facial recognition, and anomaly detection in industrial IoT systems
- 4K Video Processing: Perform hardware-accelerated H.264/H.265 encoding for surveillance systems, live streaming servers, and video transcoding applications
- Robotics Control: Execute complex motion planning algorithms and sensor fusion tasks with dual-core A72 processors while A53 cores handle real-time control loops
- Network Appliances: Build custom firewalls, VPN gateways, and network monitoring solutions leveraging Gigabit Ethernet and mainline Linux networking stack
- Multimedia Servers: Create DLNA/UPnP media servers with 4K video streaming capabilities and transcoding support for home automation systems
- Scientific Computing: Execute MATLAB/Octave workloads and perform data analysis with sufficient computational resources for research applications
- Kubernetes Edge Nodes: Deploy containerized microservices using Docker and Kubernetes for distributed edge computing architectures
How to Use
Begin by connecting the Khadas Edge to power via the USB Type-C port using a 5V/3A power adapter minimum, ensuring stable power delivery to prevent kernel panics. Insert a microSD card pre-flashed with Ubuntu 20.04 or Debian Bullseye using Etcher or dd command, then connect HDMI monitor, keyboard, and mouse for initial setup. The board boots into a graphical desktop environment within 30 seconds, where you can configure network settings, update the kernel, and install development tools via apt package manager. For headless deployments, configure SSH access by enabling it through raspi-config equivalent tools, then manage the board remotely over Ethernet without requiring display peripherals.
For advanced applications, access GPIO pins through sysfs interface or libgpiod library, configure I2C/SPI devices using standard Linux tools, and leverage the Mali GPU acceleration through OpenGL ES or GStreamer plugins for video processing pipelines. The 40-pin header pinout mirrors Raspberry Pi compatibility, allowing direct use of HAT accessories with minor software adjustments. For machine learning projects, install TensorFlow or PyTorch using pre-compiled ARM64 packages, then optimize inference performance by utilizing the Mali GPU through TensorFlow Lite GPU delegate or ONNX Runtime. Monitor system temperature via /sys/class/thermal interfaces and implement active cooling scripts if sustained operation exceeds 70 degrees Celsius in your deployment environment.
Frequently Asked Questions
What is the difference between Khadas Edge and Khadas Edge V?
The Khadas Edge V features an upgraded Rockchip RK3399Pro processor with an integrated 5 TOPS TPU coprocessor optimized for neural network inference, making it superior for AI applications. The standard Edge uses the RK3399 without TPU acceleration. Both share identical RAM, storage options, and I/O interfaces. Choose Edge V if your project requires dedicated AI acceleration; choose standard Edge for general-purpose computing and video processing workloads.
Can I run Docker and Kubernetes on Khadas Edge?
Yes, the Khadas Edge fully supports Docker and Kubernetes through mainline Linux kernel. Install Docker using standard ARM64 repositories, then deploy lightweight Kubernetes distributions like K3s or MicroK8s for edge computing clusters. The 4GB RAM is sufficient for small containerized workloads; for production deployments with multiple containers, consider clustering multiple Edge boards. GPU workloads require additional NVIDIA Container Toolkit configuration adapted for Mali GPU architecture.
What storage options are available and can I upgrade?
The Khadas Edge comes with 32GB or 64GB eMMC storage soldered to the board, which cannot be upgraded. However, you can expand storage using microSD cards up to 512GB or external USB 3.0 storage devices. For applications requiring higher I/O performance, connect NVMe SSDs via USB 3.0 adapter, achieving read speeds up to 400MB/s. The eMMC is suitable for OS and applications; use external storage for media libraries and database files.
Is the Khadas Edge suitable for 24/7 continuous operation?
Yes, the Khadas Edge is engineered for continuous operation with proper thermal management. The copper heat spreader maintains temperatures below 70 degrees Celsius under sustained load with ambient cooling. For extended 24/7 deployments, implement active cooling using a 5V fan connected to GPIO PWM pins, monitor temperature via sysfs, and ensure adequate airflow around the board. The LPDDR4 RAM and eMMC storage are rated for continuous operation without degradation.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.
What is your return and warranty policy?
We offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication
- Expert Team: Our technical team validates every product before listing
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available
- Technical Support: 24/7 expert guidance via email and WhatsApp
- Returns: 7-day return policy on manufacturing defects only
Buy Khadas Edge Online in India
Purchase the Khadas Edge online at The Engineer Store, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat. Get the best price on
Khadas Edge
The Khadas Edge is a high-performance ARM-based single-board computer featuring a Rockchip RK3399 hexa-core processor, designed for demanding embedded applications, AI inference, and multimedia processing. Professional developers and systems integrators use the Khadas Edge for edge computing deployments, robotics projects, and real-time video processing applications requiring superior computational power compared to traditional SBCs. This board solves the critical problem of delivering desktop-class performance in a compact form factor while maintaining low power consumption for always-on edge computing scenarios.
Product Overview
The Khadas Edge represents a significant advancement in single-board computing, powered by the Rockchip RK3399 processor which integrates dual ARM Cortex-A72 cores clocked at 1.8GHz and quad ARM Cortex-A53 cores at 1.4GHz, complemented by a dedicated Mali-T860 MP4 GPU. This heterogeneous multi-core architecture enables asymmetric processing where computationally intensive tasks leverage the high-performance A72 cores while background tasks utilize the efficient A53 cores, resulting in optimal power efficiency. The board includes 4GB of LPDDR4 RAM and supports up to 64GB eMMC storage, providing ample resources for complex applications including machine learning frameworks, containerized workloads, and real-time video encoding at 4K resolution.
What distinguishes the Khadas Edge from competing platforms is its comprehensive I/O ecosystem and professional-grade thermal management. The board features dual USB 3.0 ports for high-speed data transfer, Gigabit Ethernet for network connectivity, HDMI 2.0 output supporting 4K@60Hz video, and a 40-pin GPIO header compatible with Raspberry Pi peripherals. Advanced cooling through a copper heat spreader and optional active cooling ensures sustained performance under continuous load. The device runs mainline Linux kernels and supports Ubuntu, Debian, and Android operating systems, making it ideal for developers requiring upstream kernel support and long-term software maintenance without vendor lock-in.
Key Specifications
| Specification | Details |
| Product Type | ARM-based Single Board Computer |
| Brand | Khadas |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| Processor | Rockchip RK3399 Hexa-core (2x ARM Cortex-A72 @ 1.8GHz, 4x ARM Cortex-A53 @ 1.4GHz) |
| GPU | Mali-T860 MP4 with OpenGL ES 3.2 support |
| RAM | 4GB LPDDR4 (dual-channel) |
| Storage | 32GB or 64GB eMMC 5.1 with optional microSD card expansion |
| Video Output | HDMI 2.0 (4K@60Hz), MIPI DSI connector for LCD panels |
| Connectivity | Gigabit Ethernet, Dual USB 3.0, USB Type-C, 40-pin GPIO header |
| Power Consumption | 5-15W typical operation, 20W peak |
Key Features
- Rockchip RK3399 Hexa-core Processor delivering 2.4x performance improvement over quad-core alternatives for parallel workload execution
- Mali-T860 MP4 GPU with 4 execution engines enabling hardware-accelerated video encoding/decoding and 3D graphics rendering
- Dual USB 3.0 ports providing 5Gbps bandwidth for high-speed data transfer and external storage connectivity
- Mainline Linux kernel support ensuring long-term software stability and security updates without vendor dependency
- 40-pin GPIO header with PWM, SPI, I2C, and UART interfaces for seamless integration with industrial sensors and actuators
- MIPI DSI and CSI connectors for direct integration with camera modules and LCD displays without USB overhead
- Professional thermal design with copper heat spreader maintaining stable operation under sustained 100% CPU load
- Gigabit Ethernet with optional WiFi/Bluetooth module support for robust network-connected edge deployments
Applications and Use Cases
- Edge AI Inference: Deploy pre-trained TensorFlow Lite and ONNX models for real-time object detection, facial recognition, and anomaly detection in industrial IoT systems
- 4K Video Processing: Perform hardware-accelerated H.264/H.265 encoding for surveillance systems, live streaming servers, and video transcoding applications
- Robotics Control: Execute complex motion planning algorithms and sensor fusion tasks with dual-core A72 processors while A53 cores handle real-time control loops
- Network Appliances: Build custom firewalls, VPN gateways, and network monitoring solutions leveraging Gigabit Ethernet and mainline Linux networking stack
- Multimedia Servers: Create DLNA/UPnP media servers with 4K video streaming capabilities and transcoding support for home automation systems
- Scientific Computing: Execute MATLAB/Octave workloads and perform data analysis with sufficient computational resources for research applications
- Kubernetes Edge Nodes: Deploy containerized microservices using Docker and Kubernetes for distributed edge computing architectures
How to Use
Begin by connecting the Khadas Edge to power via the USB Type-C port using a 5V/3A power adapter minimum, ensuring stable power delivery to prevent kernel panics. Insert a microSD card pre-flashed with Ubuntu 20.04 or Debian Bullseye using Etcher or dd command, then connect HDMI monitor, keyboard, and mouse for initial setup. The board boots into a graphical desktop environment within 30 seconds, where you can configure network settings, update the kernel, and install development tools via apt package manager. For headless deployments, configure SSH access by enabling it through raspi-config equivalent tools, then manage the board remotely over Ethernet without requiring display peripherals.
For advanced applications, access GPIO pins through sysfs interface or libgpiod library, configure I2C/SPI devices using standard Linux tools, and leverage the Mali GPU acceleration through OpenGL ES or GStreamer plugins for video processing pipelines. The 40-pin header pinout mirrors Raspberry Pi compatibility, allowing direct use of HAT accessories with minor software adjustments. For machine learning projects, install TensorFlow or PyTorch using pre-compiled ARM64 packages, then optimize inference performance by utilizing the Mali GPU through TensorFlow Lite GPU delegate or ONNX Runtime. Monitor system temperature via /sys/class/thermal interfaces and implement active cooling scripts if sustained operation exceeds 70 degrees Celsius in your deployment environment.
Frequently Asked Questions
What is the difference between Khadas Edge and Khadas Edge V?
The Khadas Edge V features an upgraded Rockchip RK3399Pro processor with an integrated 5 TOPS TPU coprocessor optimized for neural network inference, making it superior for AI applications. The standard Edge uses the RK3399 without TPU acceleration. Both share identical RAM, storage options, and I/O interfaces. Choose Edge V if your project requires dedicated AI acceleration; choose standard Edge for general-purpose computing and video processing workloads.
Can I run Docker and Kubernetes on Khadas Edge?
Yes, the Khadas Edge fully supports Docker and Kubernetes through mainline Linux kernel. Install Docker using standard ARM64 repositories, then deploy lightweight Kubernetes distributions like K3s or MicroK8s for edge computing clusters. The 4GB RAM is sufficient for small containerized workloads; for production deployments with multiple containers, consider clustering multiple Edge boards. GPU workloads require additional NVIDIA Container Toolkit configuration adapted for Mali GPU architecture.
What storage options are available and can I upgrade?
The Khadas Edge comes with 32GB or 64GB eMMC storage soldered to the board, which cannot be upgraded. However, you can expand storage using microSD cards up to 512GB or external USB 3.0 storage devices. For applications requiring higher I/O performance, connect NVMe SSDs via USB 3.0 adapter, achieving read speeds up to 400MB/s. The eMMC is suitable for OS and applications; use external storage for media libraries and database files.
Is the Khadas Edge suitable for 24/7 continuous operation?
Yes, the Khadas Edge is engineered for continuous operation with proper thermal management. The copper heat spreader maintains temperatures below 70 degrees Celsius under sustained load with ambient cooling. For extended 24/7 deployments, implement active cooling using a 5V fan connected to GPIO PWM pins, monitor temperature via sysfs, and ensure adequate airflow around the board. The LPDDR4 RAM and eMMC storage are rated for continuous operation without degradation.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India.
What is your return and warranty policy?
We offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication
- Expert Team: Our technical team validates every product before listing
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available
- Technical Support: 24/7 expert guidance via email and WhatsApp
- Returns: 7-day return policy on manufacturing defects only
Buy Khadas Edge Online in India
Purchase the Khadas Edge online at The Engineer Store, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat. Get the best price on