Learning About Jetson Orin Nano Base Board: Grades, Material Standards, and Industrial Applications

Types of NVIDIA Jetson Orin Nano Base Boards

The NVIDIA Jetson Orin Nano Base Board is a powerful edge AI computing platform designed for intelligent machines, robotics, and computer vision applications. Built on the advanced Orin system-on-chip (SoC), these boards deliver exceptional performance in compact form factors. With multiple configurations available, users can choose the ideal model based on memory capacity, integration level, and development needs.

Jetson Orin Nano Base Board

The standard Orin Nano base board delivers high-speed performance with advanced AI capabilities. It leverages the NVIDIA Orin SoC architecture to support complex deep learning and AI inference tasks in real time.

Advantages

• High-performance AI processing
• Compact design for embedded systems
• Energy-efficient operation
• Ideal for robotics and automation

Considerations

• Requires additional components for full setup
• May need cooling solutions under heavy load

Best for: Embedded AI applications, custom robotics, edge computing projects

Jetson Orin Nano 8GB Base Board

This high-memory variant features 8 GB of LPDDR5 memory, enabling it to handle demanding AI workloads such as real-time object detection, multi-sensor fusion, and high-resolution video analytics.

Advantages

• 80-core NVIDIA GPU for graphics-intensive tasks
• Dual-core ARM Carmel CPU for efficient computation
• Suitable for complex neural networks
• Excellent multitasking performance

Considerations

• Higher power consumption than 4GB model
• Premium pricing

Best for: Advanced AI development, autonomous machines, high-resolution vision systems

Jetson Orin Nano 4GB Base Board

A cost-effective, streamlined version with 4 GB of memory, optimized for entry-level AI applications and lightweight edge computing tasks.

Advantages

• Lower cost for budget-conscious projects
• Energy efficient with reduced thermal output
• Sufficient for basic AI inference
• Great for learning and prototyping

Limitations

• Limited memory for large models
• Not ideal for multi-stream video processing

Best for: Educational projects, simple automation, IoT edge devices

Jetson Orin Nano with Camera Module

An integrated solution featuring an onboard camera, designed specifically for computer vision and image processing applications.

Advantages

• Ready-to-use vision capabilities
• Enables real-time object recognition
• Reduces need for external cameras
• Perfect for visual AI prototyping

Limitations

• Camera specs may not suit all use cases
• Less flexibility in sensor choice

Best for: Computer vision projects, surveillance systems, visual robotics

Jetson Orin Nano Developer Kit

A complete out-of-the-box development platform that includes the base board, power supply, and interface accessories, enabling rapid AI application prototyping and testing.

Advantages

• All-in-one development solution
• Quick setup and deployment
• Official NVIDIA software support
• Ideal for beginners and professionals

Considerations

• Higher initial investment
• Bulkier than standalone boards

Best for: AI developers, researchers, startups, rapid prototyping

Jetson Orin Nano with Robotics Kit

A comprehensive robotics development package that combines the Orin Nano base board with sensors, actuators, motors, and mounting hardware for building intelligent robots.

Advantages

• Integrated robotics components
• Accelerates robot development
• Suitable for navigation and manipulation tasks
• Leverages Orin’s powerful AI engine

Limitations

• May include unnecessary components for some users
• Requires technical knowledge to assemble

Best for: Robotics engineers, academic projects, autonomous mobile robots (AMRs)

Model	Memory	AI Performance	Use Case	Target Users
Orin Nano Base Board	4GB/8GB Options	High	Custom Embedded AI	Developers, Engineers
Orin Nano 8GB	8 GB	Very High	Advanced AI Workloads	AI Researchers, Professionals
Orin Nano 4GB	4 GB	Moderate	Entry-Level AI	Students, Hobbyists
With Camera Module	4GB/8GB	High (Vision-Optimized)	Computer Vision	Vision Developers
Developer Kit	4GB/8GB	High	Rapid Prototyping	New & Experienced Devs
With Robotics Kit	4GB/8GB	High	Robot Construction	Robotics Teams, Educators

Expert Tip: When selecting a Jetson Orin Nano model, consider not only memory and performance but also I/O requirements, power constraints, and software compatibility. For production deployments, the 8GB model offers better future-proofing for AI model updates and scalability.

Materials in NVIDIA Jetson Orin Nano Base Boards

The materials used in the construction of NVIDIA Jetson Orin Nano base boards play a crucial role in determining the device’s performance, thermal management, electrical efficiency, and long-term reliability. These components are carefully selected to meet the demands of AI edge computing, robotics, and embedded systems. Below is a detailed breakdown of the key materials and their functional significance.

Copper

Copper is a foundational material in the Jetson Orin Nano’s printed circuit board (PCB), primarily used in circuit traces and internal wiring. Its exceptional electrical conductivity minimizes resistance, enabling efficient power delivery and high-speed signal transmission across the board. This ensures stable operation of the high-performance GPU and CPU, especially under sustained computational loads typical in AI inference and machine learning applications.

Silicon

Silicon forms the core of all semiconductor components on the board, including the NVIDIA Orin SoC (System on Chip). High-purity silicon wafers are doped with precise amounts of boron or phosphorus to create p-type and n-type regions, forming transistors, diodes, and integrated circuits. Silicon’s semiconducting properties allow for precise control of electrical currents, making it indispensable for modern microelectronics and digital logic operations.

Aluminum

Aluminum is widely used in heat sinks and thermal management systems due to its excellent thermal conductivity and lightweight nature. On the Jetson Orin Nano, aluminum heat sinks are strategically placed over the SoC and power regulators to dissipate heat efficiently. This passive cooling solution helps maintain optimal operating temperatures, preventing thermal throttling and ensuring consistent performance in compact or enclosed environments.

Plastic (Polycarbonate & Other Polymers)

Various plastic materials, particularly polycarbonate, are used for connectors, insulating housings, and mounting fixtures. These non-conductive materials provide electrical insulation and mechanical protection for sensitive circuitry. Polycarbonate is favored for its high impact resistance, dimensional stability, and ability to withstand moderate heat, making it ideal for ruggedized enclosures and connector bodies in industrial and outdoor applications.

FR-4 Epoxy Glass Fiber

FR-4 is the standard substrate material for the Jetson Orin Nano’s multilayer PCB. This composite of woven fiberglass cloth and flame-retardant epoxy resin offers excellent mechanical rigidity, electrical insulation, and resistance to high temperatures encountered during soldering and operation. Its durability ensures the board remains structurally sound and electrically reliable, even under thermal cycling and mechanical stress.

Gold

Gold is applied as a thin plating on connectors, edge contacts, and critical signal pads. Its primary advantage lies in its resistance to oxidation and corrosion, ensuring long-term reliability of electrical connections. Gold-plated contacts maintain low contact resistance and prevent signal degradation, which is essential for high-frequency data transmission and stable board-to-peripheral communication in mission-critical applications.

Steel

Steel is utilized in mounting brackets, screws, and structural supports due to its high tensile strength and durability. These mechanical components secure the base board within enclosures or robotic platforms, protecting against vibration, shock, and physical displacement. Stainless steel variants may be used in harsh environments to resist rust and chemical exposure.

Tantalum

Tantalum capacitors are employed in power regulation circuits for their high capacitance per volume, excellent stability, and low leakage current. Tantalum’s ability to form a stable oxide layer makes it highly resistant to heat and corrosion, ensuring reliable performance over extended periods. These capacitors help smooth voltage fluctuations and support clean power delivery to sensitive components like the GPU and memory modules.

Material	Primary Function	Key Properties
Copper	Electrical conduction in traces and vias	High conductivity, ductility, thermal transfer
Silicon	Semiconductor substrate for chips	Controllable conductivity, scalable fabrication
Aluminum	Heat dissipation in cooling systems	Lightweight, high thermal conductivity
Plastic (Polycarbonate)	Insulation and structural housing	Durable, non-conductive, heat-resistant
FR-4 Epoxy Glass Fiber	PCB substrate	Mechanically strong, electrically insulating, flame-retardant
Gold	Contact plating for connectors	Corrosion-resistant, low resistance, reliable
Steel	Mechanical support and mounting	High strength, durable, vibration-resistant
Tantalum	Capacitors in power circuits	High capacitance, stable, heat/corrosion resistant

Why Material Selection Matters

Performance Optimization: Materials like copper and silicon directly influence processing speed, power efficiency, and signal integrity—critical for AI and real-time computing tasks.

Thermal Management: Aluminum heat sinks and thermally conductive substrates prevent overheating, extending component lifespan and maintaining peak performance.

Durability & Reliability: FR-4, steel, and tantalum ensure the board can endure mechanical stress, environmental exposure, and long operational cycles without failure.

Signal Integrity: Gold-plated contacts and high-quality PCB materials reduce noise and signal loss, ensuring stable communication between components.

Design Flexibility: Lightweight plastics and compact capacitors allow for smaller, more efficient designs suitable for drones, robots, and portable AI devices.

Note: The combination of these advanced materials enables the NVIDIA Jetson Orin Nano to deliver powerful edge AI capabilities in a compact, energy-efficient form factor. Understanding their roles helps engineers make informed decisions when integrating the module into custom systems, especially in demanding environments such as industrial automation, autonomous vehicles, and smart cities.

Commercial Value of Jetson Baseboards

NVIDIA Jetson baseboards have emerged as a cornerstone in the development of intelligent edge computing systems, offering powerful, compact, and energy-efficient solutions for a wide range of high-impact industries. Their integration of GPU-accelerated AI processing with real-time computing capabilities makes them indispensable in modern commercial applications. Below are the key sectors where Jetson baseboards deliver substantial value.

Robotics

Jetson base kits are instrumental in building advanced robots capable of complex tasks such as autonomous navigation, environmental perception, and object manipulation. Their high-performance computing power enables robots to operate effectively in dynamic and unpredictable environments.

• Used in industrial automation for material handling and assembly line operations
• Power delivery robots in logistics and last-mile distribution networks
• Integrated into robotic vacuum cleaners and floor scrubbers for smart home and commercial cleaning
• Support sensor fusion (LiDAR, cameras, IMUs) for robust localization and path planning

Key benefit: Enables real-time AI inference for responsive and adaptive robotic behavior

Edge AI Computing

Jetson platforms excel in edge AI by enabling on-device data processing without reliance on cloud infrastructure. This reduces latency, enhances privacy, and improves reliability in mission-critical applications.

• Deployed in AI-powered security cameras for facial recognition and anomaly detection
• Used in drones for onboard object tracking and autonomous flight control
• Integrated into smart sensors for predictive analytics in remote or offline environments
• Supports low-latency inference for time-sensitive decision-making

Strategic advantage: Eliminates bandwidth dependency and ensures data sovereignty

Computer Vision

Jetson baseboards are specifically optimized for computer vision workloads, supporting real-time object detection, image segmentation, and video analytics at high frame rates and resolutions.

• Applied in traffic monitoring systems to detect congestion and violations
• Used in retail analytics for customer behavior tracking and shelf monitoring
• Deployed in healthcare for medical imaging analysis and surgical assistance systems
• Supports frameworks like TensorFlow, PyTorch, and TensorRT for rapid AI model deployment

Performance highlight: Capable of processing 4K video streams with multiple AI models simultaneously

Industrial IoT

Jetson boards serve as intelligent hubs in Industrial IoT ecosystems, connecting sensors, cameras, and machinery for real-time monitoring, analytics, and predictive maintenance.

• Installed in manufacturing plants to monitor equipment health and prevent unplanned downtime
• Enable quality control through automated visual inspection systems
• Facilitate digital twin integration by processing live operational data
• Improve worker safety with AI-powered hazard detection and alert systems

Operational impact: Increases OEE (Overall Equipment Effectiveness) and reduces maintenance costs

Autonomous Systems

One of the most transformative applications of Jetson baseboards is in autonomous vehicles and drones, where real-time processing of sensor data is critical for safe navigation and decision-making.

• Equipped with powerful GPUs for simultaneous processing of LiDAR, radar, and camera inputs
• Enable SLAM (Simultaneous Localization and Mapping) for indoor and outdoor navigation
• Used in agricultural drones for crop monitoring and precision spraying
• Support autonomous last-mile delivery robots and mobile platforms

Innovation driver: Accelerates development of safe, scalable autonomous mobility solutions

Smart Cities

Jetson technology plays a pivotal role in building smarter, safer, and more sustainable urban environments by processing vast amounts of real-time urban data at the edge.

• Deployed in intelligent traffic management to optimize signal timing and reduce congestion
• Used in public safety systems for crowd monitoring and emergency response coordination
• Integrated into environmental monitoring stations for air quality and noise level analysis
• Supports energy-efficient city operations through data-driven resource allocation

Societal benefit: Enhances quality of life while reducing environmental footprint

Business Insight: Companies leveraging Jetson baseboards gain a competitive edge through faster time-to-market, reduced cloud dependency, and enhanced system reliability. When positioning these solutions to clients, emphasize total cost of ownership, scalability, and future-proofing through software-upgradable AI models.

Application Sector	Key Use Cases	AI/ML Workloads Supported	Commercial Impact
Robotics	Autonomous navigation, manipulation, delivery	Object detection, path planning, sensor fusion	30–50% efficiency gain in logistics and automation
Edge AI Computing	On-device inference, real-time analytics	Facial recognition, anomaly detection	Latency reduction up to 90% vs. cloud-only systems
Computer Vision	Video analytics, image segmentation	YOLO, Mask R-CNN, DeepStream pipelines	Improved accuracy in retail and healthcare diagnostics
Industrial IoT	Predictive maintenance, quality control	Time-series analysis, defect detection	Upto 40% reduction in unplanned downtime
Autonomous Systems	Self-driving vehicles, drone navigation	SLAM, depth estimation, obstacle avoidance	Enables next-gen mobility and logistics solutions
Smart Cities	Traffic optimization, public safety	Behavioral analytics, environmental modeling	15–30% improvement in urban service efficiency

Additional Strategic Advantages

• Software Ecosystem: Full support for NVIDIA JetPack SDK, including CUDA, cuDNN, and TensorRT for accelerated AI development
• Scalability: From Jetson Nano to AGX Orin, scalable performance for diverse application needs
• Energy Efficiency: High compute performance per watt, ideal for battery-powered and embedded systems
• Developer Community: Extensive documentation, pre-trained models, and active forums accelerate product development
• Certifications: Meets industrial standards (IP ratings, extended temperature ranges) for rugged deployments

How to Choose the Right NVIDIA Jetson Board

Selecting the ideal NVIDIA Jetson development board is crucial for maximizing performance, scalability, and return on investment in AI and edge computing projects. With a range of options available—from the compact Jetson Nano to the powerful Jetson AGX Orin—making an informed decision based on your specific project requirements ensures long-term success. This comprehensive guide outlines the key factors to consider when choosing a Jetson board, helping developers, engineers, and hobbyists alike make the best choice for their applications.

Important Note: The NVIDIA Jetson platform is designed for AI at the edge, including robotics, computer vision, autonomous machines, and IoT applications. Choosing the right model impacts not only performance but also power efficiency, thermal management, and future scalability.

Key Factors to Consider When Selecting a Jetson Board

1. Application Requirements and Workload Complexity

   The nature of your project should be the primary driver in selecting a Jetson board. Different models offer varying levels of computational power tailored to specific use cases:

   • Lightweight Tasks (e.g., basic image processing, sensor data logging): The Jetson Nano or Jetson Xavier NX 4GB variant provides sufficient performance for entry-level AI inference and computer vision tasks.
   • Intermediate Workloads (e.g., object detection, multi-sensor fusion): The Jetson Xavier NX 8GB offers enhanced GPU and CPU capabilities, enabling smooth execution of more complex models like YOLO or ResNet.
   • Advanced AI & Deep Learning (e.g., real-time semantic segmentation, autonomous navigation): For high-throughput, low-latency applications, the Jetson AGX Orin series delivers up to 275 TOPS of AI performance, making it ideal for robotics and industrial automation.
2. Power Consumption and Thermal Management

   Jetson boards vary significantly in power draw and heat generation, which directly affect deployment environments:

   • The Jetson Nano operates at 5–10W, making it suitable for battery-powered or embedded systems with passive cooling.
   • The Jetson Xavier NX consumes 10–15W and typically requires active cooling (e.g., heatsink with fan) in sustained workloads.
   • The Jetson AGX Orin can draw up to 50W under full load and demands robust thermal solutions, especially in enclosed or high-temperature environments.
   • Consider ambient temperature, enclosure design, and duty cycle when planning cooling strategies to prevent throttling and ensure stable performance.
3. Project Budget and Cost Efficiency

   Cost is a critical factor, especially for startups, educational institutions, or large-scale deployments:

   • Entry-Level Projects: The Jetson Nano Developer Kit (~$99) offers excellent value for learning AI and prototyping simple applications.
   • Mid-Range Development: The Jetson Xavier NX (~$399) balances performance and price for commercial prototypes and small production runs.
   • High-End Industrial Use: The Jetson AGX Orin (~$1,000+) is justified for mission-critical systems requiring maximum AI throughput and reliability.
   • Always evaluate total cost of ownership, including accessories, power supply, and integration effort, not just the board price.
4. Future-Proofing and Expandability

   Anticipate how your project might evolve over time:

   • Choose boards with available PCIe lanes, M.2 slots, and GPIO expansion headers to support future upgrades (e.g., additional cameras, storage, or networking).
   • The Jetson AGX Orin supports multiple high-speed interfaces (e.g., 10GbE, USB 3.2, CAN FD), enabling integration with advanced peripherals.
   • Consider software compatibility across the Jetson family—code developed on Nano can often scale to Orin with minimal changes, thanks to NVIDIA’s consistent JetPack SDK.
   • Selecting a higher-tier board initially may save redesign costs later if your AI model complexity increases.
5. Development Support and Ecosystem

   NVIDIA provides a mature ecosystem that accelerates development:

   • JetPack SDK: Includes Linux OS, CUDA, cuDNN, TensorRT, and Vision Programming Interface (VPI), streamlining AI deployment.
   • Docker & Container Support: Enables reproducible environments and easy deployment across devices.
   • NVIDIA Developer Forums and Documentation: Extensive tutorials, sample code, and community support reduce learning curves.
   • Jetson Inference and DeepStream: Pre-built tools for deploying vision models and video analytics pipelines.
   • Ensure your team has access to training resources or prior experience with CUDA and AI frameworks to maximize productivity.
6. Availability of Accessories and Peripherals

   Access to compatible hardware components is essential for rapid prototyping and deployment:

   • Camera Modules: Official Arducam and Leopard Imaging cameras are optimized for Jetson CSI interfaces. Verify compatibility with your chosen board’s MIPI lanes.
   • Cooling Solutions: Active heatsinks and fan kits are widely available for Xavier NX and Orin models.
   • Power Supplies: Use regulated 5V/4A or 19V adapters depending on the model to avoid instability.
   • Carrier Boards: Third-party carriers (e.g., from ConnectTech or Auvidea) offer ruggedized designs with additional I/O for industrial use.
   • Check supplier lead times and regional availability, especially for global deployments.

Jetson Model	AI Performance (TOPS)	RAM	Power Consumption	Ideal Use Cases
Jetson Nano	0.5	4GB LPDDR4	5–10W	Education, basic AI, hobby robotics
Jetson Xavier NX (4GB)	21	4GB LPDDR4x	10–15W	Smart cameras, drones, entry-level edge AI
Jetson Xavier NX (8GB)	21	8GB LPDDR4x	10–15W	Multi-sensor AI, industrial automation
Jetson AGX Orin (32GB)	200	32GB LPDDR5	15–50W	Autonomous machines, medical imaging, robotics
Jetson AGX Orin (64GB)	275	64GB LPDDR5	15–50W	High-performance AI, data centers at the edge

Expert Tip: Start with a developer kit (like Jetson Nano or Xavier NX) for prototyping and proof-of-concept. Once validated, transition to production-grade modules (e.g., Jetson Orin NX or AGX Orin) using official carrier boards or custom designs for scalability and reliability.

Final Recommendations

• Always benchmark your AI model on the target hardware using TensorRT to assess real-world inference speed and latency.
• Leverage NVIDIA TAO Toolkit to fine-tune pre-trained models, reducing the need for high-end hardware in some cases.
• Join the NVIDIA Developer Program for early access to SDK updates, technical support, and certification tools.
• Consider using Jetson Flash and OTA updates for remote device management in fleet deployments.
• Validate mechanical and electrical compatibility early—especially for custom enclosures and power delivery systems.

Choosing the right NVIDIA Jetson board is a strategic decision that balances performance, power, cost, and future needs. By carefully evaluating your application requirements and growth trajectory, you can select a platform that not only meets current demands but also scales with your innovation. When in doubt, consult NVIDIA’s official Jetson Module Selector or reach out to authorized partners for tailored recommendations.

Frequently Asked Questions About Jetson Orin Nano Base Boards

Q. What is the difference between the Jetson Orin Nano 4GB and 8GB base boards?

The primary distinction between the Jetson Orin Nano 4GB and 8GB base boards is their onboard memory (RAM) capacity, which directly impacts performance and application suitability:

• Jetson Orin Nano 4GB: Designed for entry-level and cost-sensitive AI and edge computing projects. It efficiently handles lightweight workloads such as basic neural network inference, simple robotics, and low-resolution image processing. Ideal for developers and hobbyists starting out with AI applications.
• Jetson Orin Nano 8GB: Offers double the memory, enabling more complex multitasking and support for larger AI models. This version is better suited for advanced applications like real-time object detection, multi-sensor data fusion, higher-resolution computer vision tasks, and running multiple containers or services simultaneously.

Additionally, the 8GB model often provides improved thermal headroom and sustained performance under load, making it a preferred choice for commercial prototypes and performance-critical deployments.

Q. Which projects are the Jetson Orin Nano 4GB Base Board ideal for?

The Jetson Orin Nano 4GB Base Board is an excellent platform for beginners and developers working on lightweight AI and automation tasks. It excels in:

• Home Automation: Integrating voice control, smart lighting, and sensor-based environmental monitoring using platforms like Home Assistant or custom Python scripts.
• Basic Image Classification: Running pre-trained models (e.g., MobileNet) to identify objects in images or video streams from USB or CSI cameras.
• Entry-Level Robotics: Powering small robots with obstacle avoidance, line following, or gesture recognition using simple sensor arrays.
• Educational Projects: Teaching AI, machine learning, and embedded systems concepts in classrooms or DIY learning environments.
• IoT Edge Devices: Processing sensor data locally before sending it to the cloud, reducing latency and bandwidth usage.

Thanks to its energy efficiency and compact design, the 4GB model is perfect for always-on, low-power applications where high computational throughput isn’t required.

Q. What accessories can be attached to the Jetson Orin Nano Base Board using the GPIO?

The Jetson Orin Nano Base Board features a 40-pin GPIO (General Purpose Input/Output) header, similar to the Raspberry Pi, offering extensive hardware expandability. This allows integration with a wide range of peripherals and sensors, including:

• Sensors: Temperature (e.g., DS18B20), humidity, motion (PIR), proximity (ultrasonic), light, gas, and environmental sensors for smart monitoring systems.
• Actuators: Servo motors, relays, solenoids, and motor drivers for robotics, automation, and control systems.
• Displays: Small OLED, LCD, or TFT screens for local status monitoring and user interfaces.
• Communication Modules: Wi-Fi, Bluetooth, LoRa, or GPS modules via UART, SPI, or I²C interfaces.
• External Storage: EEPROMs, SD card adapters, or FRAM modules for data logging and backup.
• User Input Devices: Keypads, buttons, rotary encoders, and touch panels for interactive applications.

With support for multiple communication protocols (I²C, SPI, UART, PWM, and GPIO interrupts), the board enables robust and flexible hardware interfacing, making it ideal for prototyping custom edge computing solutions.

Q. What cooling options are available for the Jetson Orin Nano Base Boards?

To maintain optimal performance and prevent thermal throttling, the Jetson Orin Nano supports various cooling solutions depending on workload intensity:

• Passive Cooling (Heat Sinks): Most standard configurations include a metal heat spreader or aluminum heat sink. This is sufficient for light to moderate workloads and silent operation, ideal for home or office environments.
• Active Air Cooling (Fans): For sustained high-performance tasks—such as running deep learning models or video analytics—a small fan mounted on the heat sink improves heat dissipation and maintains stable clock speeds.
• Enclosure-Integrated Cooling: Some third-party cases come with built-in fans and ventilation channels designed specifically for the Orin Nano form factor.
• Liquid Cooling (Advanced Use Cases): While rare, custom liquid cooling setups can be implemented in extreme edge computing scenarios requiring maximum thermal efficiency and minimal noise.

NVIDIA recommends monitoring system temperature via jtop or system logs, especially during intensive AI inference, to ensure long-term reliability and performance stability.

Q. How does the Jetson Orin Nano handle power consumption during idle and active states?

The Jetson Orin Nano is engineered for energy efficiency across operating conditions, leveraging NVIDIA’s power-optimized architecture:

• Idle State: When not actively processing tasks, the board enters a low-power mode, drawing as little as 1–2 watts. This makes it suitable for battery-powered or always-on applications like security cameras or environmental sensors.
• Active State: Power consumption scales dynamically based on workload. Light tasks (e.g., sensor reading) may use 3–5W, while heavy AI inference or video processing can increase usage to 10–15W depending on configuration and peripherals.
• Power Modes: The Orin Nano supports configurable power modes (via NVIDIA’s Power Mode settings) that allow developers to balance performance and energy use—ideal for optimizing for either speed or battery life.
• Efficient Architecture: Built on a 12nm process and featuring an ARM CPU with dedicated AI accelerators (NVIDIA Ampere architecture with Tensor Cores), the board delivers high compute per watt, making it one of the most efficient edge AI platforms available.

This adaptive power management ensures reliable operation in diverse environments—from portable devices to industrial systems—without sacrificing performance when needed.