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Khadas Edge-V
The Khadas Edge-V is a high-performance ARM-based single-board computer powered by the Rockchip RK3399Pro hexa-core processor, designed for edge computing, AI inference, and embedded Linux applications. Professional developers, roboticists, and AI engineers use this platform to prototype machine learning models, build IoT gateways, and deploy real-time computer vision systems in resource-constrained environments. It solves the critical problem of requiring desktop-grade computational power in a compact form factor while maintaining low power consumption for edge deployment scenarios.
Product Overview
The Khadas Edge-V leverages the Rockchip RK3399Pro System-on-Module architecture, which integrates dual Cortex-A72 cores (up to 1.8GHz) and quad Cortex-A53 cores (up to 1.4GHz) alongside a dedicated Mali-T860 MP4 GPU for graphics acceleration. The built-in Neural Processing Unit (NPU) delivers 5 TOPS of AI compute performance, enabling efficient inference of TensorFlow Lite, ONNX, and MobileNet models without requiring external accelerators. The board features 4GB LPDDR4 RAM and 16GB eMMC storage as standard, with dual-channel memory architecture supporting 1600MHz bandwidth for memory-intensive workloads. This combination of CPU, GPU, and NPU creates a versatile platform that outperforms Raspberry Pi 4 in computational tasks while maintaining compatibility with standard Linux distributions and development frameworks.
The Edge-V distinguishes itself through its robust I/O connectivity including USB 3.0 Type-C, dual USB 2.0 ports, Gigabit Ethernet, HDMI 2.0 output, 3.5mm audio jack, and a 40-pin GPIO header for hardware interfacing. The board supports multiple operating systems including Android 9, Ubuntu 20.04, and Debian, providing flexibility for diverse application requirements. Thermal management is handled through a passive heatsink design, making it suitable for fanless deployment in noise-sensitive environments. The open-source community support and extensive documentation from Khadas ensure developers can rapidly prototype and scale their projects from proof-of-concept to production deployment.
Key Specifications
| Specification | Details |
| Product Type | ARM-based Single Board Computer with NPU |
| 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 RK3399Pro Hexa-core (2x Cortex-A72 @ 1.8GHz + 4x Cortex-A53 @ 1.4GHz) |
| RAM | 4GB LPDDR4 (1600MHz dual-channel) |
| Storage | 16GB eMMC, microSD card slot for expansion |
| GPU | Mali-T860 MP4 with OpenGL ES 3.2 support |
| NPU | Dedicated Neural Processing Unit - 5 TOPS AI performance |
| Connectivity | USB 3.0 Type-C, Dual USB 2.0, Gigabit Ethernet, HDMI 2.0, 3.5mm Audio |
| GPIO | 40-pin header with I2C, SPI, UART, PWM support |
| Power Consumption | 5-10W typical operation |
| Operating Systems | Android 9, Ubuntu 20.04, Debian Linux |
Key Features
- RK3399Pro Hexa-core Processor: Delivers 2-3x performance improvement over Raspberry Pi 4 with optimized power efficiency for continuous edge computing deployments
- Integrated 5 TOPS NPU: Accelerates AI inference tasks including object detection, image classification, and pose estimation without external GPU requirements
- Mali-T860 MP4 GPU: Enables hardware-accelerated graphics rendering and parallel processing for multimedia and scientific computing applications
- USB 3.0 Type-C Interface: Provides 5Gbps data transfer rates for high-speed peripheral connectivity and faster development workflows
- 40-pin GPIO Header: Full compatibility with standard Linux GPIO libraries and hardware protocols including I2C, SPI, UART for sensor integration
- Dual-Channel LPDDR4 Memory: 1600MHz bandwidth supports memory-intensive workloads and multithreaded application execution
- Fanless Thermal Design: Passive heatsink cooling eliminates noise for deployment in audio-sensitive environments and reduces maintenance requirements
- Multi-OS Support: Flexibility to run Android, Ubuntu, or Debian based on application requirements with seamless cross-platform development
Applications and Use Cases
- Edge AI Inference: Deploy pre-trained neural networks for real-time object detection, facial recognition, and anomaly detection in surveillance systems without cloud dependency
- Robotics and Autonomous Systems: Process sensor data from cameras, LiDAR, and IMU sensors with sufficient computational headroom for autonomous navigation and control algorithms
- IoT Gateway and Data Processing: Aggregate data from multiple sensors, perform local filtering and preprocessing, and transmit only relevant information to cloud platforms for bandwidth optimization
- Embedded Media Server: Run Kodi, Plex, or custom streaming applications with hardware-accelerated video decoding supporting 4K content delivery
- Industrial Automation: Control manufacturing equipment, monitor process parameters, and execute real-time control logic with deterministic performance characteristics
- Computer Vision Research: Prototype and validate OpenCV-based vision algorithms with sufficient performance for real-time video processing at 1080p or higher resolutions
How to Use
Begin by selecting your preferred operating system from Khadas' official repository. Download the appropriate image file (Android, Ubuntu, or Debian) and flash it to the 16GB eMMC using the USB 3.0 Type-C connection with the Khadas USB Burning Tool on your development machine. Once the OS is installed, connect the Edge-V to your network via Gigabit Ethernet or configure WiFi through the system settings. For GPIO-based hardware projects, install the Khadas GPIO Python library and reference the pinout diagram provided in the documentation to map your sensors and actuators to the 40-pin header.
For AI inference workloads, install TensorFlow Lite or ONNX runtime optimized for the RK3399Pro NPU. Convert your pre-trained models to the appropriate format using the Khadas Model Conversion Tool, which automatically optimizes for the integrated NPU's quantization requirements. Test your model performance using the provided benchmark scripts to measure inference latency and power consumption. The Khadas community forums and GitHub repository contain extensive examples for computer vision, robotics, and IoT applications that can accelerate your development cycle. For production deployments, implement proper thermal monitoring and configure the CPU frequency scaling governor to balance performance and power consumption based on your workload characteristics.
Frequently Asked Questions
What is the difference between Khadas Edge-V and Khadas Edge?
The Edge-V features the RK3399Pro processor with an integrated NPU providing 5 TOPS of AI acceleration, while the standard Edge uses the RK3399 without NPU capabilities. The Edge-V is specifically optimized for machine learning inference tasks, making it superior for AI applications. Both share similar I/O connectivity and form factors, but the Edge-V's dedicated neural processing unit makes it 5-10x faster for inference workloads compared to CPU-only execution on the standard Edge.
Can I run Docker containers on the Khadas Edge-V?
Yes, Docker is fully supported on Ubuntu and Debian installations. The ARM64 architecture is compatible with most containerized applications, though you should verify that your specific container images support ARMv8 architecture. We recommend using lightweight container distributions like Alpine Linux to optimize storage utilization on the 16GB eMMC. For production deployments, consider expanding storage via microSD card to accommodate multiple container images and persistent data volumes.
What is the maximum power consumption and thermal output?
Under typical operation, the Edge-V consumes 5-10W with peak consumption reaching 15W during full CPU and GPU utilization. The passive heatsink design dissipates this heat effectively without active cooling, maintaining junction temperatures below 80 degrees Celsius in standard ambient conditions. For continuous high-performance workloads, ensure adequate airflow around the board and monitor thermal output using the built-in temperature sensors accessible through the sysfs interface.
Is the Khadas Edge-V suitable for real-time applications?
The Edge-V runs standard Linux kernels which are not real-time by default. For deterministic real-time requirements below 10ms latency, consider using the PREEMPT_RT kernel patch or exploring dedicated real-time extensions. For most robotics, IoT, and edge AI applications with latency requirements in the 50-500ms range, the standard kernel provides sufficient performance. If you require hard real-time guarantees, consult our technical team for alternative solutions or custom kernel configurations.
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-V Online in India
Khadas Edge-V
The Khadas Edge-V is a high-performance ARM-based single-board computer powered by the Rockchip RK3399Pro hexa-core processor, designed for edge computing, AI inference, and embedded Linux applications. Professional developers, roboticists, and AI engineers use this platform to prototype machine learning models, build IoT gateways, and deploy real-time computer vision systems in resource-constrained environments. It solves the critical problem of requiring desktop-grade computational power in a compact form factor while maintaining low power consumption for edge deployment scenarios.
Product Overview
The Khadas Edge-V leverages the Rockchip RK3399Pro System-on-Module architecture, which integrates dual Cortex-A72 cores (up to 1.8GHz) and quad Cortex-A53 cores (up to 1.4GHz) alongside a dedicated Mali-T860 MP4 GPU for graphics acceleration. The built-in Neural Processing Unit (NPU) delivers 5 TOPS of AI compute performance, enabling efficient inference of TensorFlow Lite, ONNX, and MobileNet models without requiring external accelerators. The board features 4GB LPDDR4 RAM and 16GB eMMC storage as standard, with dual-channel memory architecture supporting 1600MHz bandwidth for memory-intensive workloads. This combination of CPU, GPU, and NPU creates a versatile platform that outperforms Raspberry Pi 4 in computational tasks while maintaining compatibility with standard Linux distributions and development frameworks.
The Edge-V distinguishes itself through its robust I/O connectivity including USB 3.0 Type-C, dual USB 2.0 ports, Gigabit Ethernet, HDMI 2.0 output, 3.5mm audio jack, and a 40-pin GPIO header for hardware interfacing. The board supports multiple operating systems including Android 9, Ubuntu 20.04, and Debian, providing flexibility for diverse application requirements. Thermal management is handled through a passive heatsink design, making it suitable for fanless deployment in noise-sensitive environments. The open-source community support and extensive documentation from Khadas ensure developers can rapidly prototype and scale their projects from proof-of-concept to production deployment.
Key Specifications
| Specification | Details |
| Product Type | ARM-based Single Board Computer with NPU |
| 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 RK3399Pro Hexa-core (2x Cortex-A72 @ 1.8GHz + 4x Cortex-A53 @ 1.4GHz) |
| RAM | 4GB LPDDR4 (1600MHz dual-channel) |
| Storage | 16GB eMMC, microSD card slot for expansion |
| GPU | Mali-T860 MP4 with OpenGL ES 3.2 support |
| NPU | Dedicated Neural Processing Unit - 5 TOPS AI performance |
| Connectivity | USB 3.0 Type-C, Dual USB 2.0, Gigabit Ethernet, HDMI 2.0, 3.5mm Audio |
| GPIO | 40-pin header with I2C, SPI, UART, PWM support |
| Power Consumption | 5-10W typical operation |
| Operating Systems | Android 9, Ubuntu 20.04, Debian Linux |
Key Features
- RK3399Pro Hexa-core Processor: Delivers 2-3x performance improvement over Raspberry Pi 4 with optimized power efficiency for continuous edge computing deployments
- Integrated 5 TOPS NPU: Accelerates AI inference tasks including object detection, image classification, and pose estimation without external GPU requirements
- Mali-T860 MP4 GPU: Enables hardware-accelerated graphics rendering and parallel processing for multimedia and scientific computing applications
- USB 3.0 Type-C Interface: Provides 5Gbps data transfer rates for high-speed peripheral connectivity and faster development workflows
- 40-pin GPIO Header: Full compatibility with standard Linux GPIO libraries and hardware protocols including I2C, SPI, UART for sensor integration
- Dual-Channel LPDDR4 Memory: 1600MHz bandwidth supports memory-intensive workloads and multithreaded application execution
- Fanless Thermal Design: Passive heatsink cooling eliminates noise for deployment in audio-sensitive environments and reduces maintenance requirements
- Multi-OS Support: Flexibility to run Android, Ubuntu, or Debian based on application requirements with seamless cross-platform development
Applications and Use Cases
- Edge AI Inference: Deploy pre-trained neural networks for real-time object detection, facial recognition, and anomaly detection in surveillance systems without cloud dependency
- Robotics and Autonomous Systems: Process sensor data from cameras, LiDAR, and IMU sensors with sufficient computational headroom for autonomous navigation and control algorithms
- IoT Gateway and Data Processing: Aggregate data from multiple sensors, perform local filtering and preprocessing, and transmit only relevant information to cloud platforms for bandwidth optimization
- Embedded Media Server: Run Kodi, Plex, or custom streaming applications with hardware-accelerated video decoding supporting 4K content delivery
- Industrial Automation: Control manufacturing equipment, monitor process parameters, and execute real-time control logic with deterministic performance characteristics
- Computer Vision Research: Prototype and validate OpenCV-based vision algorithms with sufficient performance for real-time video processing at 1080p or higher resolutions
How to Use
Begin by selecting your preferred operating system from Khadas' official repository. Download the appropriate image file (Android, Ubuntu, or Debian) and flash it to the 16GB eMMC using the USB 3.0 Type-C connection with the Khadas USB Burning Tool on your development machine. Once the OS is installed, connect the Edge-V to your network via Gigabit Ethernet or configure WiFi through the system settings. For GPIO-based hardware projects, install the Khadas GPIO Python library and reference the pinout diagram provided in the documentation to map your sensors and actuators to the 40-pin header.
For AI inference workloads, install TensorFlow Lite or ONNX runtime optimized for the RK3399Pro NPU. Convert your pre-trained models to the appropriate format using the Khadas Model Conversion Tool, which automatically optimizes for the integrated NPU's quantization requirements. Test your model performance using the provided benchmark scripts to measure inference latency and power consumption. The Khadas community forums and GitHub repository contain extensive examples for computer vision, robotics, and IoT applications that can accelerate your development cycle. For production deployments, implement proper thermal monitoring and configure the CPU frequency scaling governor to balance performance and power consumption based on your workload characteristics.
Frequently Asked Questions
What is the difference between Khadas Edge-V and Khadas Edge?
The Edge-V features the RK3399Pro processor with an integrated NPU providing 5 TOPS of AI acceleration, while the standard Edge uses the RK3399 without NPU capabilities. The Edge-V is specifically optimized for machine learning inference tasks, making it superior for AI applications. Both share similar I/O connectivity and form factors, but the Edge-V's dedicated neural processing unit makes it 5-10x faster for inference workloads compared to CPU-only execution on the standard Edge.
Can I run Docker containers on the Khadas Edge-V?
Yes, Docker is fully supported on Ubuntu and Debian installations. The ARM64 architecture is compatible with most containerized applications, though you should verify that your specific container images support ARMv8 architecture. We recommend using lightweight container distributions like Alpine Linux to optimize storage utilization on the 16GB eMMC. For production deployments, consider expanding storage via microSD card to accommodate multiple container images and persistent data volumes.
What is the maximum power consumption and thermal output?
Under typical operation, the Edge-V consumes 5-10W with peak consumption reaching 15W during full CPU and GPU utilization. The passive heatsink design dissipates this heat effectively without active cooling, maintaining junction temperatures below 80 degrees Celsius in standard ambient conditions. For continuous high-performance workloads, ensure adequate airflow around the board and monitor thermal output using the built-in temperature sensors accessible through the sysfs interface.
Is the Khadas Edge-V suitable for real-time applications?
The Edge-V runs standard Linux kernels which are not real-time by default. For deterministic real-time requirements below 10ms latency, consider using the PREEMPT_RT kernel patch or exploring dedicated real-time extensions. For most robotics, IoT, and edge AI applications with latency requirements in the 50-500ms range, the standard kernel provides sufficient performance. If you require hard real-time guarantees, consult our technical team for alternative solutions or custom kernel configurations.
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