With the rapid development of AI technology, more and more edge computing devices can handle tasks ranging from lightweight to complex AI models. In this article, we will compare several mainstream edge AI devices, including NVIDIA Jetson series, Orange Pi, and Raspberry Pi 5, and explore the potential of the Hailo accelerator in the edge AI field. We will focus on analyzing the computing power, power consumption, memory, and supported AI model types of each device to help developers choose the most suitable edge AI solution.

Conclusion and recommendations at the end 👉🏻

NVIDIA Jetson Series: Comprehensive Support for Various AI Models

Comparison of Edge AI Computing Solutions: From NVIDIA Jetson to Hailo Accelerator

With the rapid development of artificial intelligence (AI) technology, more and more edge computing devices can handle tasks ranging from lightweight to complex AI models. In this article, we will compare several mainstream edge AI devices, including NVIDIA Jetson series, Orange Pi, and Raspberry Pi 5, and explore the potential of the Hailo accelerator in the edge AI field. We will focus on analyzing the computing power, power consumption, memory, and supported AI model types of each device to help developers choose the most suitable edge AI solution.

NVIDIA Jetson Series: Comprehensive Support for Various AI Models

NVIDIA Jetson series is one of the most powerful AI computing solutions on the current edge computing market. Thanks to NVIDIA’s powerful GPU and optimized ecosystem, the Jetson series supports deep learning, computer vision, and some complex AI models. With Jetson Containers, developers can easily run AI models from mainstream frameworks such as TensorFlow, PyTorch, and ONNX on the device.

Advantages

• Multi-framework support: Jetson devices can run mainstream frameworks such as TensorFlow, PyTorch, ONNX, and easily deploy different types of models through containers.
• Optimized inference performance: Through TensorRT and CUDA, inference latency can be significantly reduced, making it possible to run complex models on edge devices.
• Mature ecosystem: A wealth of development tools and community support make the Jetson series very suitable for various AI applications from research to commercialization.

Things to Note

• Limitations of running large models: Although Jetson devices have powerful performance, running large language models is still challenging, and model size and device memory need to be considered.
• Power consumption and heat dissipation: High-performance devices like Jetson Orin NX 16GB have relatively high power consumption, requiring consideration of heat dissipation and power supply.

Real-world cases:

• Stable Diffusion: Running Stable Diffusion on Jetson Orin Nano takes about 2 minutes to generate a 512×512 image (25 steps).
• LLM deployment: Running a small LLM requires at least 13GB of memory, which can be reduced after quantization, but performance will be affected. For example, the INT4 version of the Llama3.2 1B model only requires 0.75GB of video memory, meaning that even the smallest 4GB version of Orin Nano can handle it.

Orange Pi: High Cost-Performance Edge Computing Solution

Orange Pi is known for its high cost-performance ratio, suitable for lightweight AI model inference. The latest Orange Pi AI Pro series has significantly improved performance, offering various computing power versions to meet different AI application needs.

Advantages

• High cost-performance ratio: Compared to the Jetson series, Orange Pi devices are more affordable, suitable for small projects or prototype development.
• Multiple computing power options: The Orange Pi AI Pro offers multiple computing power versions to choose from based on project needs.

Disadvantages

• Limited ecosystem support: Development tools and community resources are relatively scarce, which may require more time for development and optimization.
• Official information is limited: Detailed specifications and performance for high computing power versions have not been provided by the official, requiring further verification.

References:

• Orange Pi AI Pro (8-12 TOPS) official page
• Orange Pi AI Pro (8-12 TOPS) parameter page
• Orange Pi AI Pro (20 TOPS) parameter page
• CSDN Blog: Orange Pi AI Pro is coming strong
• Huawei Developer Forum discussion
• Medium – OrangePi AiPro: review and guide
• The strongest development board, can 3588 also do AIO? A 10,000-word evaluation of the 32G memory Orange Pi 5 Plus

Raspberry Pi 5 and Hailo Accelerator Combination: Enhancing Inference Performance

Raspberry Pi 5 is a popular DIY and educational tool. By integrating the Hailo-8L or Hailo-8 AI accelerator, the Raspberry Pi can run medium-sized AI models on edge devices. Hailo-8L provides up to 13 TOPS of computing power, while Hailo-8 offers 26 TOPS, significantly enhancing the inference performance of the Raspberry Pi, especially in image processing and object detection tasks.

It is important to note that the Hailo-8 and Hailo-8L may use the 8GB RAM of the Raspberry Pi 5, which needs to be considered when running large models.

Advantages

• Strong community support: Raspberry Pi has a wide user base, with abundant resources and tutorials, making it suitable for education and prototype development.
• Performance enhancement: After integrating Hailo-8L or Hailo-8, the AI inference capability is significantly improved, suitable for various visual applications.

Disadvantages

• Memory limitations: According to the Hailo-10H M.2 product brief, the Hailo accelerator may rely on the Raspberry Pi’s system memory, which needs further verification.
• Limited support for generative AI models: Hailo-8L and Hailo-8 currently mainly support visual inference tasks and do not support language models and generative AI models.
• Need for additional hardware: Requires purchasing and integrating the Hailo accelerator, increasing complexity and cost.

References:

• Hailo-8™ AI Processor
• Hailo Model Zoo
• Raspberry Pi 5 Specifications
• Hailo-10H M.2 module product brief

Outlook: Hailo-10H in Generative AI Applications

Hailo-10H is Hailo’s new generation AI accelerator, aimed at enhancing the inference capability of edge devices for generative AI models. Compared to the Hailo-8 series, Hailo-10H claims to be able to run complex generative AI models, including certain language models and generative models.

Potential of Hailo-10H

• Support for generative AI: Hailo-10H is designed to support generative AI models, such as certain language models and image generation models.
• High performance-to-power ratio: While providing high computing power, power consumption remains below 25W, suitable for power-constrained edge devices and embedded systems.
• Modular design: With M.2 modular design, it is easy to integrate into existing hardware, widely applicable in scenarios such as autonomous driving, smart monitoring, and industrial IoT.

Things to Note

• Actual support situation: As of now, Hailo’s Model Zoo has not provided support for generative AI models, and updates should be monitored.
• Maturity of ecosystem: Compared to NVIDIA’s ecosystem, Hailo’s development tools and community support are still being improved.
• Memory dependency: Hailo-10H may use the host device’s system memory, so ensure the device has enough RAM.

References: Hailo’s latest AI chip shows up integrated NPUs and sips power like fine wine

Conclusion

In the field of edge AI computing, the NVIDIA Jetson series dominates with its powerful GPU and mature ecosystem, supporting various mainstream AI model types. NVIDIA’s camp promotes Jetson Xavier NX and Jetson Orin Nano (the latter having twice the computing power of the former), as they are somewhat affordable.

Orange Pi provides a high cost-performance option, especially with the newly launched Orange Pi AI Pro series, offering developers more computing power choices. However, it should be noted that Orange Pi’s ecosystem and community support are relatively limited, and support for large complex models needs further verification.

For those on a budget or lightweight applications, the Raspberry Pi 5 combined with the Hailo-8L or Hailo-8 accelerator is a very good choice, significantly enhancing visual inference performance. This combination has strong scalability, robust community support, and ultra-energy efficiency. The only downside is that it does not support large language models (LLM) such as Stable Diffusion and other generative AI models. We look forward to the future release of Hailo-10H to fill this gap.

When choosing an edge AI computing solution, developers need to comprehensively consider the device’s computing power, memory, power consumption, price, and ecosystem support to meet specific application requirements.

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