The Raspberry Pi 5 is significantly faster than its predecessor — a 2–3× CPU improvement. However, for real-time inference of detection, classification, or text generation, a hardware accelerator is often mandatory. We have been deploying AI on Pi for over 5 years and have helped dozens of projects move from prototype to production. For example, a client from the electronics industry wanted to detect scratches on a circuit board in real time. Without an accelerator, YOLOv8n delivered 15 FPS — insufficient for a 30 cm/s conveyor. After optimization with Hailo-8 and INT8 quantization, we achieved stable 110 FPS. According to Wikipedia, Edge AI is an approach that processes data locally without latency, which is critical for industrial tasks. Below are practical recommendations.
Which Accelerator to Choose for Raspberry Pi 5?
There are three main options on the market:
| Accelerator | Performance (TOPS) | Power Consumption | Model Support | Works with Pi 5 |
|---|---|---|---|---|
| Hailo-8 HAT+ (M.2) | 26 TOPS | 5 W | Any (via Hailo SDK) | Yes (M.2 slot via HAT) |
| Google Coral USB Accelerator | 4 TOPS | 2–3 W | Only INT8 TFLite | Yes (USB, Pi 4/5) |
| Intel Neural Compute Stick 2 | 1 TOPS | 1–2 W | OpenVINO, outdated | Partially |
The Hailo-8 is the choice today: 26 TOPS at minimal power consumption. In our projects, it delivers 120+ FPS on YOLOv8n. Coral is a budget option for ready-made TFLite models. Intel NCS2 is only found in legacy systems.
How Hailo-8 Affects Performance
| Model | Without Accelerator (Pi 5 CPU) | With Hailo-8 |
|---|---|---|
| YOLOv8n (detection) | ~30 FPS | 120+ FPS |
| MobileNetV3 (classification) | ~15 FPS | 60+ FPS |
| Llama 3.2 1B (generation) | 8–12 tokens/s | — (not yet supported) |
The difference is 4–5×. For real-time tasks (video surveillance, robotics), an accelerator is mandatory. Budget savings with this approach can reach 40% compared to cloud solutions. For a typical project, the total cost ranges from $2,000 to $5,000, including hardware, optimization, and deployment. Over three years, this solution saves up to 40% compared to cloud inference, with full data privacy.
Stack Without Accelerator (Pure Pi 5)
If the task is not time-critical, the CPU with TFLite + XNNPACK (ARM Neon) suffices. For NLP — Llama.cpp using transformer-based models: Llama 3.2 1B runs at 8–12 tokens/s. That is enough for an offline assistant or simple classification. For CV tasks, you can use MobileNetV3-SSD: 8–10 FPS at 320×320 resolution. But if you need latency < 100 ms, YOLOv8n on Hailo-8 is essential.
How We Optimize Models and Deploy AI
From Our Practice: Defectoscopy Case
Client — electronics manufacturer. Needed real-time scratch detection on circuit boards. Problems: YOLOv8n model was heavy for Pi 5 (15 FPS), heat dissipation. We:
- Performed INT8 quantization using Hailo SDK — FPS rose to 110.
- Configured pipeline via GStreamer, reducing p99 latency to 30 ms.
- Added CPU throttling to avoid overheating. Result: stable 30 FPS on the conveyor, 99.9% reliability.
We specialize in model optimization for Raspberry Pi platforms, applying quantization-aware training and optimizing runtime kernels for ARM Neon.
Process of Work
Deployment Stages:
- Analysis: load, latency requirements, accelerator selection.
- Design: inference pipeline architecture.
- Implementation: quantization, model conversion, SDK setup.
- Testing: FPS measurements, p99 latency, thermal stress.
- Deployment: deploy on Pi, monitoring, documentation.
What Is Included in the Work?
- Selection of hardware accelerator and components.
- Model optimization (quantization, conversion) for the specific SDK.
- System setup (OS, drivers, libraries).
- Integration of inference pipeline (GStreamer, OpenCV, etc.).
- Performance testing and stress test.
- Documentation and operation instructions.
- Training of your team (up to 2 hours).
- Post-deployment support (1 month).
Common Mistakes When Deploying AI on Pi
- Ignoring heat dissipation — throttling reduces FPS.
- Using FP32 models instead of INT8 quantized models with proper calibration.
- Non-optimized input/output pipeline (GStreamer is mandatory).
- Incorrect model selection: overly heavy architectures (YOLOv8m) give 5 FPS even with an accelerator due to memory bandwidth limits.
- Lack of temperature monitoring — at 85°C, Pi drops its frequency.
Why Trust Us with Deployment?
We have over 5 years of experience in edge AI and more than 50 successful projects on Raspberry Pi. We integrate edge MLOps practices for continuous model updates and provide a documented performance guarantee. Get a consultation — contact us, and we will assess your project within one day. The Hailo HAT+ is a high-performance accelerator we recommend for Raspberry Pi 5. Request an evaluation of your project. We will help select an accelerator and optimize the model for your requirements.
Edge AI is the entry point to smart devices without the cloud.