With the rapid development of AI technology, more and more edge computing devices can handle tasks ranging from lightweight tasks 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 types of AI models for each device, helping 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 tasks 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 types of AI models for each device, helping 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 in the current edge computing market. Thanks to NVIDIA’s powerful GPU and optimized ecosystem, the Jetson series supports a range of tasks from deep learning, computer vision to certain complex AI models. Through Jetson Containers, developers can easily run AI models from mainstream frameworks such as TensorFlow, PyTorch, ONNX on the device.
| Device Name | Computing Power (TOPS) | GPU Architecture | Memory | CPU | Power Consumption Range | Supported Model Types | Advantages |
|---|---|---|---|---|---|---|---|
| Jetson Nano | 0.5 TOPS | Maxwell (128 cores) | 4GB | 4 core ARM Cortex-A57 | 5W-10W | Lightweight models, visual inference | Suitable for small projects, lightweight inference tasks |
| Jetson Xavier NX | 21 TOPS | Volta (384 cores) | 8GB | 6 core ARM v8.2 64-bit CPU | 10W-15W | Computer vision, deep learning | Balance of power consumption and performance, suitable for complex models |
| Jetson Orin Nano | 40 TOPS | Ampere (512 cores) | 4GB/8GB | 6 core ARM Cortex-A78AE | 7W-15W | Deep learning, speech recognition | Medium power consumption, suitable for medium tasks |
| Jetson Orin NX | 70-100 TOPS | Ampere (1024 cores) | 8GB/16GB | 6 core ARM Cortex-A78AE | 10W-25W | Large deep learning, complex models | Powerful computing capabilities, supports large inference tasks |
Advantages
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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 for complex models to run on edge devices. -
Mature Ecosystem: A rich set of development tools and community support make the Jetson series very suitable for various AI applications from research to commercialization.
Points to Note
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Limitations in Running Large Models: Although Jetson devices are powerful, running large language models is still challenging and requires consideration of model size and device memory. Reference -
Power Consumption and Heat Dissipation: High-performance devices like Jetson Orin NX 16GB have higher power consumption, requiring consideration of heat dissipation and power supply.
Real Case Studies:
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Stable Diffusion: Running Stable Diffusion on Jetson Orin Nano takes about 2 minutes to generate a 512×512 image (25 steps). Reference -
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 VRAM, meaning even the minimum memory version of 4GB Orin Nano can handle it. Reference
Orange Pi: High Cost-Performance Edge Computing Solution
Orange Pi is known for its high cost-performance ratio and is 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.
| Device Name | Computing Power (TOPS) | GPU Architecture | Memory | CPU | Power Consumption Range | Supported Model Types | Disadvantages |
|---|---|---|---|---|---|---|---|
| Orange Pi 5 Plus (RK3588 with TPU) | 6 TOPS | Mali-G610 MP4 | 4GB-32GB | 4 core Cortex-A76 + 4 core Cortex-A55 | 7-10W | Image recognition, lightweight AI models | Limited computing power, unable to run large models |
| Orange Pi AI Pro (8-12 TOPS) | 8-12 TOPS | Integrated graphics processor | 8GB/16GB | 4 core 64-bit processor + AI processor | 7-10W (Medium Review) | Image recognition, deep learning, language models (user tested 1token/second) | Limited official information, further verification needed |
| Orange Pi AI Pro (20 TOPS) | 20 TOPS | Integrated graphics processor | 12GB/24GB | 4 core 64-bit processor + AI processor | Unknown | Deep learning, complex models | Limited official information, further verification needed |
Advantages
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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
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Limited Ecosystem Support: Development tools and community resources are relatively scarce, which may require more time for development and optimization. -
Limited Official Information: Detailed specifications and performance for high computing power versions have not been fully provided by the official source, further verification is needed.
References:
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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 Strong Arrival -
Huawei Developer Forum Discussion -
Medium – OrangePi AiPro: review and guide -
The Strongest Development Board, Can 3588 Do AIO? A Ten Thousand Word Evaluation of Orange Pi 5Plus with 32G Memory
Combining Raspberry Pi 5 with Hailo Accelerator: Enhancing Inference Performance
Raspberry Pi 5 is a popular DIY and educational tool. By integrating the Hailo-8L or Hailo-8 AI accelerator, Raspberry Pi can run medium-sized AI models on edge devices. Hailo-8L provides up to 13 TOPS of computing power, while Hailo-8 provides 26 TOPS, significantly enhancing Raspberry Pi’s inference performance, especially in image processing and object detection tasks.
It is worth noting that Hailo-8 and Hailo-8L may use the 8GB RAM of Raspberry Pi 5, which needs to be considered when running large models. Reference
| Device Name | Computing Power (TOPS) | GPU Architecture | Memory | CPU | Power Consumption Range | Supported Model Types | Disadvantages |
|---|---|---|---|---|---|---|---|
| Raspberry Pi 5 + Hailo-8L | 13 TOPS | VideoCore VII | 4GB/8GB | 4 core ARM Cortex-A76 | About 8W (Hailo-8L 1.5W) | Visual models, object detection | Limited support for large generative models |
| Raspberry Pi 5 + Hailo-8 | 26 TOPS | VideoCore VII | 4GB/8GB | 4 core ARM Cortex-A76 | About 10W (Hailo-8 2.5W) | Visual models, object detection | Limited support for large generative models |
Advantages
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Strong Community Support: Raspberry Pi has a broad user base, with abundant resources and tutorials, making it suitable for education and prototype development. -
Performance Improvement: After integrating Hailo-8L or Hailo-8, the AI inference capability has significantly improved, suitable for various visual applications.
Disadvantages
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Memory Limitations: Based on speculation from the Hailo-10H M.2 product brief, the Hailo accelerator may depend on the system memory of the Raspberry Pi, which needs further verification. Reference -
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. Reference -
Additional Hardware Required: Requires purchase and integration of Hailo accelerator, increasing complexity and cost.
References:
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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, designed to enhance the inference capabilities of edge devices in 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.
| Device Name | Computing Power (TOPS) | Supported Model Types | Power Consumption | Advantages |
|---|---|---|---|---|
| Hailo-10H | 40 TOPS | Generative AI, language models | Expected < 5W | Enhances the ability of edge devices to run complex AI models with low power consumption |
Potential of Hailo-10H
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Support for Generative AI: Hailo-10H aims to support generative AI models, such as certain language models and image generation models. Reference -
High Performance Ratio: While providing high computing power, the 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.
Points to Note
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Actual Support Situation: As of now, Hailo’s Model Zoo has not provided support for generative AI models, requiring attention to subsequent updates. Reference -
Ecosystem Maturity: Compared to NVIDIA’s ecosystem, Hailo’s development tools and community support are still being improved. -
Memory Dependency: Hailo-10H may use the system memory of the host device, ensuring that the device has enough RAM is essential. Reference
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 main push is for the Jetson Xavier NX and Jetson Orin Nano (the latter’s computing power is twice that of the former), as the price is somewhat acceptable.
Orange Pi offers a high cost-performance option, especially the newly launched Orange Pi AI Pro series, providing developers with more computing power options. However, it should be noted that the ecosystem and community support for Orange Pi are relatively limited, and support for large complex models needs further verification.
For those on a budget or lightweight applications, the combination of Raspberry Pi 5 with the Hailo-8L or Hailo-8 accelerator is an excellent choice, significantly enhancing visual inference performance. This combination has strong scalability, robust community support, and energy efficiency. The only drawback is that it does not support large language models (LLM) such as Stable Diffusion and other generative AI models. We look forward to the release of Hailo-10H to fill this gap.
When selecting an edge AI computing solution, developers need to consider the computing power, memory, power consumption, price, and ecosystem support of the device to meet the specific application needs.
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