DIY Electronics

Raspberry Pi 5 AI HAT+ 2: Edge Computing for Makers in 2026

By Dr. David Zhang Published

Raspberry Pi 5 AI HAT+ 2: Edge Computing for Makers in 2026

The Raspberry Pi ecosystem has always been about putting real computing power in the hands of makers and educators. With the release of the AI HAT+ 2, Raspberry Pi has made a decisive move into the world of generative AI and edge computing — running large language models and vision-language models directly on a $90 setup, with no cloud dependency.

This is a significant upgrade from the kinds of projects we explored at the Maker Faire years ago. The hardware has gotten dramatically more capable while staying affordable.

What Is the AI HAT+ 2?

The AI HAT+ 2 is an add-on board for the Raspberry Pi 5 that provides dedicated AI acceleration hardware. Here are the key specs:

  • Processor: Hailo-10H neural network accelerator
  • Performance: 40 TOPS (INT4) of inferencing capability
  • Dedicated RAM: 8GB on-board for model weights
  • Interface: M.2 HAT+ connector to Raspberry Pi 5
  • Price: ~$70 for the AI HAT+ 2 module

The previous AI HAT+ used the Hailo-8L chip at 13 TOPS and had no dedicated RAM, limiting it to smaller models. The 8GB of dedicated memory on the AI HAT+ 2 is what enables it to efficiently handle LLMs and generative AI applications.

What Can You Actually Run?

According to Raspberry Pi’s official blog, the AI HAT+ 2 can accelerate:

Large Language Models

Small but capable LLMs like Llama 3.2 (1B-3B parameters) run at usable speeds for conversational AI. This means you can build a private chatbot that processes queries entirely on your desk — no API calls, no cloud dependency, no data leaving your network.

Vision-Language Models

Models that combine image understanding with text generation can run locally, enabling:

  • Describe what a camera sees in natural language
  • Answer questions about images or documents
  • Visual inspection and quality control

Object Detection and Classification

YOLOv8 runs at approximately 30fps according to Seeed Studio’s project roundup, making real-time object detection practical for:

  • Security camera analysis without cloud recording
  • Wildlife monitoring and identification
  • Automated inventory tracking
  • Safety helmet detection on construction sites

Project: Private AI Assistant

Difficulty: Intermediate | Time: 3-4 hours | Cost: ~$160 total

Build a fully offline AI assistant that runs a local LLM, responds to voice commands, and controls your smart home — all without sending any data to the cloud.

Hardware

  • Raspberry Pi 5 (8GB) — $80
  • AI HAT+ 2 — $70
  • USB microphone — $10
  • 3.5mm speaker — $5
  • MicroSD card (64GB+) — $10

Software Stack

  1. Raspberry Pi OS — base operating system
  2. Ollama — local LLM serving
  3. Whisper.cpp — local speech-to-text
  4. Piper TTS — local text-to-speech
  5. Home Assistant — smart home integration

The AI HAT+ 2 handles the LLM inference while the Pi 5’s ARM cores handle speech processing. Response times are 2-4 seconds for typical queries — not as fast as cloud-based assistants, but entirely private.

This builds on the same philosophy as the ESP32 smart home projects — controlling your own devices with hardware you own and understand.

Project: Real-Time Object Detection Camera

Difficulty: Beginner-Intermediate | Time: 2-3 hours | Cost: ~$180

Using the original AI Kit (Hailo-8L at 13 TOPS), makers have built impressive detection systems. According to Raspberry Pi’s project showcase, one Japanese maker implemented an automated system for identifying and monitoring cars at toll plazas using Edge Impulse computer vision with YOLOv5.

With the AI HAT+ 2’s 40 TOPS, you can run more complex models at higher framerates:

  • YOLOv8 nano: ~30fps for general object detection
  • YOLOv8 medium: ~15fps for more accurate detection
  • Custom fine-tuned models: Specialized detection for your specific use case

Components

  • Raspberry Pi 5 + AI HAT+ 2
  • Raspberry Pi Camera Module 3 ($25)
  • Case with camera mount ($15)

Raspberry Pi 5 vs Jetson Orin Nano for Edge AI

For makers choosing between platforms, here is a practical comparison based on MultiEmbedded’s 2026 analysis:

FeatureRaspberry Pi 5 + AI HAT+ 2Jetson Orin Nano
AI Performance40 TOPS (Hailo-10H)40 TOPS (GPU)
Price~$150~$500
Power Draw~15W~25W
EcosystemMassive (GPIO, HATs, community)NVIDIA CUDA/TensorRT
LLM SupportVia Hailo-10H NPUNative GPU inference
Best ForMaker/hobbyist projectsProfessional edge deployments

For most maker projects, the Raspberry Pi 5 with AI HAT+ 2 offers comparable performance at one-third the cost. The Jetson Orin Nano is better when you need CUDA compatibility or are deploying in a professional industrial setting.

Getting Started

If you are new to Raspberry Pi AI projects, start simple:

  1. Get the hardware: Pi 5 (8GB) + AI Kit (Hailo-8L) — ~$100 total
  2. Follow the quickstart: Raspberry Pi’s AI Kit documentation walks you through setup
  3. Run a demo: Object detection with a webcam takes about 30 minutes to set up
  4. Upgrade when ready: Move to the AI HAT+ 2 when you need more power

The Pi ecosystem rewards incremental learning. Each project builds naturally on the previous one — much like the DIY repair approach that started this site.

Sources

  1. Introducing the Raspberry Pi AI HAT+ 2: Generative AI on Raspberry Pi 5 — Raspberry Pi — accessed March 26, 2026
  2. Top Raspberry Pi AI Projects in 2026 — Seeed Studio — accessed March 26, 2026
  3. Jetson Orin Nano vs Raspberry Pi 5: Which Is Better for Edge AI in 2026 — MultiEmbedded — accessed March 26, 2026