When to Use Raspberry Pi 5 AI Kit
The Raspberry Pi 5 AI Kit delivers 82.4 FPS on YOLOv8 object detection while consuming just 9.7W-but only if you're running vision models through a camera pipeline. That specificity matters more than the impressive 13 TOPS number on the box.
I've watched dozens of developers buy this $70 kit expecting ChatGPT acceleration, only to discover it can't touch language models. The confusion is understandable: "AI Kit" sounds universal. The reality is the Hailo-8L processor is only compatible with machine-learning tasks involving the feed captured by camera modules-not webcams, not IP cameras, specifically Raspberry Pi camera modules.
This isn't a limitation; it's specialization. Computer vision inference at the edge requires fundamentally different architecture than LLM inference. The Hailo-8L's dataflow architecture excels at the former while being utterly wrong for the latter.
The Real Performance Gap: Numbers That Actually Matter
Skip the TOPS marketing. The Raspberry Pi 5's CPU runs YOLOv8 object detection at 0.45 FPS with 100% CPU utilization. Add the AI Kit and you hit 82.4 FPS at 15-30% CPU. That's not a 2x improvement-it's a 183x multiplier.
But context shapes these numbers dramatically. At PCIe Gen 3 speeds with batch size 8, the same YOLOv8s model reaches 120 FPS. Drop to Gen 2 and you're at 40 FPS. Increase batch size to 32 and performance collapses to 54 FPS.
The PCIe bottleneck is real. A single Gen 3 lane provides 8 Gbit/s-adequate for most vision tasks, but a hard ceiling. Module-based setups require all memory access to pass through the PCIe interface, unlike NPUs integrated into SoCs that share high-speed memory channels with the CPU.
For perspective: Pose estimation runs at 66.1 FPS with power draw of 9.7W total system consumption. That's 200x faster than CPU-only inference while using less power. The math checks out for battery-powered deployments.
Competing Hardware: The $70 Decision Tree
Google's Coral TPU offers 4 TOPS at 2 TOPS/W efficiency in a 6-year-old chip design. The Hailo-8L delivers 13 TOPS at 3-4 TOPS/W. On paper, Hailo wins.
But Coral has TensorFlow Lite integration that "just works." Coral's USB Accelerator connects via standard USB, integrates easily with existing systems, and supports moderate models like MobileNet v2 at about 2 watts power consumption. No PCIe configuration required.
The Hailo-8 (26 TOPS) exists but costs $150-200. At that price point, you're comparing against solutions offering more flexibility. The sweet spot is the 8L at $70-if your use case aligns.
Pineboards offers alternatives: Dual M.2 HATs that combine Hailo-8L with NVMe storage, or Coral Edge configurations for continued development on existing Coral projects. These solve the "either accelerator or storage" limitation of the official kit.
Use Case #1: Real-Time Security and Monitoring
Security cameras generate relentless data streams. The AI Kit handles 1080p security footage detecting people, cars, and packages without dropping frames. That 13x performance boost makes security cameras actually viable.
Jeff Geerling's project combined multiple Hailo NPUs-reaching 51 TOPS total by connecting Hailo-8L, Hailo-8, and Coral TPUs through PCIe switches. Excessive? Yes. But it demonstrates multi-camera scenarios at scale.
Real deployment looks different. A toll plaza monitoring system used Edge Impulse computer vision with a Camera Module Wide to detect and count vehicles across multiple lanes simultaneously. The wide lens captured broader areas; the AI Kit provided processing headroom.
Frigate NVR integration matters here. Hailo has been officially integrated into Frigate framework starting from version 0.16.0, making it a drop-in replacement for aging Coral setups in existing surveillance installations.
Critical limitation: The AI Kit and AI HAT+ do not function if there is a version mismatch between the Hailo software packages and device drivers. Production deployments need version-locking strategies.
Use Case #2: Industrial Process Control
Construction safety systems can detect humans positioned in front of, to the side of, and behind construction vehicles. AI-driven cameras replace multiple human observers and track worker locations in real time.
The advantage is parallelism: AI processes multiple danger zones simultaneously while humans naturally focus sequentially. Response time for alert generation matters more than perfect accuracy.
Manufacturing quality control follows similar logic. A production line camera checking assembly correctness needs consistent frame rates, not peak performance. The AI Kit maintains 82.4 FPS on object detection-sufficient for most manufacturing line speeds while leaving CPU capacity for control systems.
Compact size enables integration at existing production line points. The system scales by adding cameras rather than redesigning infrastructure.
But industrial deployment demands more. SD cards should be avoided for production devices due to limited write endurance and poor reliability under unreliable power. Industrial grade eMMC or hard drives are required.
Use Case #3: Robotics and Autonomous Systems
An autonomous underwater robot prototype used the AI Kit for object detection with a YOLOv8 model trained on custom datasets, coordinating with BLDC motors controlled via PCA9685 PWM driver on the I2C interface.
The challenge: integrating the Hailo SDK with existing OpenCV pipelines. Developers accustomed to 8-line PyTorch+Ultralytics implementations on PC GPUs face a steeper learning curve with Hailo's toolchain. Model conversion isn't automatic.
Navigation algorithms consume CPU cycles. Mario's hand detection system ran three models simultaneously-hand detection and landmarks-maintaining 26-28 FPS with one hand detected, 22-25 FPS with two hands. That processing budget leaves room for path planning and motor control.
Smart delivery robots exemplify the fit: continuous vision processing while the CPU handles navigation logic, communication, and decision trees. The 3-4 TOPS/W efficiency extends battery life measurably in mobile deployments.
Use Case #4: Retail and Customer Analytics
A retail supermarket management demo ran YOLOv8n on the AI Kit to detect products on shelves while EfficientNet ran on the CPU for classification. Division of labor: the NPU handles detection (where is product?), CPU handles classification (which product?).
Pose estimation adds customer behavior analysis. 66.1 FPS pose estimation performance enables tracking customer movements through store zones, dwelling time analysis, and queue detection without individual identification.
Privacy matters here. On-device processing means video never leaves the location. Models trained on generic "person" detection don't store biometric data-just spatial metadata.
The "Peeper Pam" project detected people behind you at a desk, ignoring chairs, tables, and plants in frame. Detection confidence displayed on an analog meter: 0 for "no person," 1 for "certain person present," with uncertainty in between.
That same logic applies to occupancy monitoring, queue management, and space utilization-anywhere you need "is person present?" without caring "which person?"
Use Case #5: Custom Model Deployment (With Caveats)
The Hailo Dataflow Compiler translates models from standard ML frameworks to Hailo executable format, using quantization-aware training to shrink models while maintaining accuracy.
The workflow: train in PyTorch or TensorFlow, export to ONNX, convert to HEF (Hailo Executable Format) using the DFC, deploy to the Pi. Tutorials exist for the complete training-to-deployment pipeline with YOLOv8n models.
But model compatibility isn't universal. Models compiled for Hailo are optimized specifically to chip architecture-meaning some operations simply won't map. The model zoo provides pre-compiled examples; custom architectures require testing.
The Hailo Python API now enables running inference on the Hailo-8L using Python, with examples available for both standalone scripts and integration with picamera2. This lowers the barrier compared to earlier GStreamer-only workflows.
Edge Impulse provides another path. Their platform handles the model training and Hailo conversion pipeline, outputting ready-to-deploy models. For teams without ML expertise, this managed approach reduces trial-and-error.
When NOT to Use the AI Kit
Large Language Models: The Hailo-8L processor cannot run LLMs. It's only compatible with machine-learning tasks involving camera module feeds. No amount of optimization changes this architectural limitation.
Running LLMs on Pi 5 requires CPU inference with models under 7B parameters. Gemma2-2B achieved decent performance using 3GB RAM; DeepSeek-r1:8b ran slowly. The AI Kit doesn't accelerate any of this.
Generative AI: Text generation, image synthesis, audio generation-these workflows don't map to the Hailo-8L's dataflow architecture. The future Hailo 10H with 40 TOPS and 8GB DDR4 RAM targets generative AI workloads, but isn't available for Pi 5 yet.
Non-Camera Vision Tasks: Processing still images from files works, but the AI Kit works specifically with Raspberry Pi camera modules-not webcams or IP cameras. Third-party camera compatibility requires libcamera support.
Need for Storage: The official kit's M.2 slot is occupied by the Hailo module, preventing NVMe SSD attachment. If you need both AI acceleration and fast storage, third-party dual M.2 HATs are required.
Tight Integration Requirements: As of March 2025, rpicam-apps is the only bit of Raspberry Pi's software stack deeply integrated with the Hailo accelerator. Programmatic access from Python scripts via picamera2 became available later. Early adoption meant limited API flexibility.
The Decision Framework
Ask these five questions:
1. Is your AI task vision-based?
Yes, with camera module → AI Kit is viable
No, or file-based processing → reconsider
Text/audio processing → wrong tool
2. What's your performance target?
30+ FPS real-time → AI Kit necessary
5-10 FPS acceptable → CPU might suffice
<1 FPS tolerable → don't spend $70
3. Do you need custom models?
Yes, and willing to learn DFC → manageable
Yes, but no ML expertise → Edge Impulse route
No, using pre-trained only → ideal scenario
4. What's your deployment scale?
1-10 units for prototyping → perfect fit
100+ units for production → factor in supply, thermal, reliability
Industrial/commercial → need industrial Pi variants, not retail boards
5. Can you accept the constraints?
Camera module requirement
Version dependency management
No NVMe boot without dual M.2 HAT
0-50°C operating temperature
PCIe bandwidth ceiling
If you answered favorably to questions 1, 2, and 5-and have a strategy for 3 and 4-the AI Kit delivers exceptional value at $70.
Setup Reality Check
Hardware installation takes minutes: install cooling system, attach standoffs, press GPIO header, connect ribbon cable to PCIe port, secure AI Kit with screws.
Software configuration requires more care:
sudo apt update && sudo apt full-upgrade sudo rpi-eeprom-update sudo raspi-config # Enable PCIe Gen 3 in Advanced Options sudo apt install hailo-all sudo reboot hailortcli fw-control identify # Verify installation
Version mismatches between Hailo software packages and device drivers cause complete system failure. Test thoroughly before deploying.
For best performance, using the AI Kit with the Raspberry Pi Active Cooler is recommended. Without cooling, the base RPi5 board will overheat when using the AI Kit.
Thermal management isn't optional-it's required for sustained performance.
The $70 Value Calculation
What you get:
13 TOPS neural inference
180x+ performance vs CPU-only
3-4 TOPS/W efficiency
Integrated rpicam-apps support
Pre-fitted thermal pad
All mounting hardware
What you don't get:
LLM acceleration
General-purpose AI compute
Plug-and-play simplicity
Storage expansion
Universal camera compatibility
For $70, it's hard to find a more affordable way to dip your toes into edge AI. The price undercuts Coral TPU bundles while delivering more than 3x the TOPS.
But value depends entirely on use case alignment. For vision inference at the edge, it's exceptional. For everything else, it's irrelevant.
Frequently Asked Questions
Can I use the AI Kit with Raspberry Pi 4 or earlier models?
No. The AI Kit requires the Raspberry Pi 5 as it needs native PCIe support. Earlier models lack the PCIe interface entirely. There's no workaround or adapter that changes this.
Will the AI Kit accelerate my object detection code written in Python with OpenCV?
Partially. The Hailo Python API allows running inference on the Hailo-8L using Python, but you'll need to convert your model to HEF format and modify your code to use the Hailo API instead of standard OpenCV inference calls. It's not a transparent drop-in replacement.
How does batch size affect performance?
With YOLOv8s at 640x640 resolution: batch size 2 achieves 80 FPS, batch size 4 reaches 100 FPS, batch size 8 peaks at 120 FPS. Beyond that, performance degrades: batch 16 drops to 100 FPS and batch 32 falls to 54 FPS due to PCIe bandwidth saturation.
Can I boot from NVMe and use the AI Kit simultaneously?
Not with the official kit alone. The M.2 slot is occupied by the Hailo module. Pineboards and similar vendors offer dual M.2 HATs that provide both NVMe and AI accelerator slots, solving this limitation at additional cost.
Is Google Coral support deprecated?
Not officially deprecated, but Coral's software stack hasn't been actively maintained, with PyCoral requiring Python 3.9. Google seems to have left the Coral project on life support after supply issues during the pandemic. Existing Coral hardware still works, but future support is uncertain.
What cooling do I actually need?
Raspberry Pi recommends using the AI Kit with the Active Cooler for best performance. Passive heatsinks may suffice for intermittent use, but sustained inference workloads will throttle without active cooling. Budget for the $5 Active Cooler alongside the AI Kit.
Can I run multiple camera streams simultaneously?
Yes. It is possible to run multiple neural networks on a single camera, or single or multiple neural networks with two cameras concurrently. Performance scales based on model complexity and PCIe bandwidth availability.
The Honest Conclusion
The Raspberry Pi 5 AI Kit is a specialist tool that excels within its domain. For vision inference with camera modules, it transforms the Pi 5 from "technically capable" to "actually practical" for production applications.
It's not a general-purpose AI accelerator. It won't run ChatGPT. It won't generate images. It won't help with audio synthesis. Accept these constraints and it delivers exceptional value. Fight against them and you'll waste $70.
The decision isn't "Is the AI Kit good?"-it's "Is the AI Kit right for this specific application?" Answer that honestly and you'll know whether to buy.
Key Takeaways
AI Kit delivers 82.4 FPS on YOLOv8 vs 0.45 FPS CPU-only-but only for camera-based vision tasks
Not compatible with LLMs, generative AI, or non-camera vision workflows
Requires Raspberry Pi 5 with camera module; won't work with Pi 4 or webcams
PCIe Gen 3 configuration and active cooling necessary for optimal performance
Version dependency management critical; mismatches cause complete system failure
Best for: security cameras, industrial monitoring, robotics, retail analytics
Avoid for: language models, image generation, audio processing, general AI experimentation
Data Sources
Raspberry Pi Documentation - AI Kit Software: https://www.raspberrypi.com/documentation/computers/ai.html
magazin Mehatronika - Raspberry Pi AI Kit Review: https://magazinmehatronika.com/en/raspberry-pi-ai-kit-review/
Seeed Studio - Benchmark on RPi5 & CM4: https://forums.raspberrypi.com/viewtopic.php?t=373867
Jeff Geerling - Testing Raspberry Pi's AI Kit: https://www.jeffgeerling.com/blog/2024/testing-raspberry-pis-ai-kit-13-tops-70
XDA Developers - Raspberry Pi AI Kit hands-on: https://www.xda-developers.com/raspberry-pi-ai-kit-hands-on/
Raspberry Pi Forums - AI Kit discussions: https://forums.raspberrypi.com/
Hailo Community Forums: https://community.hailo.ai/
GitHub - hailo-ai/hailo-rpi5-examples: https://github.com/hailo-ai/hailo-rpi5-examples