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The Raspberry Pi AI HAT+ 2 is a compact HAT+ add-on that gives your Raspberry Pi 5 dedicated, on-device AI acceleration for fast inference without relying on cloud services. It uses a Hailo-10H neural network accelerator plus 8GB LPDDR4X onboard memory, making it well suited to real-time computer vision, speech-to-text, and selected smaller generative AI models running locally.

Compatibility is simple: it’s Raspberry Pi 5 only, connecting via PCI Express (PCIe) for low-latency, high-bandwidth performance. It’s not designed for earlier Raspberry Pi boards (like Pi 4/3/Zero) and won’t be a fit if your project requires a generic USB accelerator. At launch it supports models including Llama-3.2-3B-Instruct, QWEN2.5-VL-3B, and Whisper (base), with support expected to expand as the software ecosystem matures.

Practical use cases include smart cameras (object detection/recognition), robotics and automation (real-time perception), offline voice transcription, and privacy-sensitive edge deployments where you don’t want data leaving the device. Compared with CPU-only inference on the Pi 5, the AI HAT+ 2 is dramatically faster and more power-efficient for supported models, and compared with USB-based accelerators it benefits from the Pi 5’s PCIe link for lower overhead and better sustained throughput. If you’re building an AI-capable Pi 5 stack, pair it with a Raspberry Pi 5 and a Raspberry Pi 5 Official Active Cooler for sustained performance: Raspberry Pi 5, Raspberry Pi 5 Official Active Cooler.

Key Features

AI Accelerator: Hailo-10H neural network accelerator Onboard Memory: 8GB LPDDR4X Interface: PCIe (via Raspberry Pi 5 PCI Express connection) Compatibility: Raspberry Pi 5 only Form Factor: HAT+ Dimensions: 56.7 × 65.1 × 5.5 mm Weight: 19 g (board only) Supported Models (launch): Llama-3.2-3B-Instruct; QWEN2.5-VL-3B; Whisper (base)

Raspberry Pi Ltd

5.0 (1 review)

· MPN: SC2166

$289.00 |
In stock
5.0 (1 review)

Powerful On-Device AI Acceleration for Raspberry Pi 5Take your Raspberry Pi projects to the next level with the Raspberry Pi AI HAT+ 2 — a compact, high-perf...

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Powerful On-Device AI Acceleration for Raspberry Pi 5
Take your Raspberry Pi projects to the next level with the Raspberry Pi AI HAT+ 2 — a compact, high-performance AI accelerator designed for real-time machine learning at the edge.
Building on the success of the original AI HAT+, this next-generation model delivers fast, efficient acceleration for computer vision, speech recognition, and selected Large Language Models (LLMs) and Vision-Language Models (VLMs) — all running locally on your Raspberry Pi 5.
Perfect for developers, educators, and innovators who want powerful AI without relying on the cloud.
🚀 Key Features
  • Hailo-10H neural network accelerator for high-efficiency AI inference
  • 8GB onboard LPDDR4X memory for demanding AI workloads
  • PCI Express connection for low-latency, high-bandwidth performance
  • Optimised for Raspberry Pi 5 (exclusive compatibility)
  • Strong vision performance comparable to the original 26 TOPS AI HAT+
  • Accelerates selected LLMs & VLMs, enabling on-device generative AI
🧠 Supported AI Models (at launch)
  • Llama-3.2-3B-Instruct
  • QWEN2.5-VL-3B
  • Whisper (base) for speech-to-text
Support continues to expand as the software ecosystem evolves.
🔧 Ideal For
  • Computer vision & image recognition
  • Speech recognition and audio AI
  • Edge AI and offline inference
  • Robotics, automation, and smart devices
  • Education, research, and rapid prototyping
📐 Technical Overview
  • Form factor: HAT+
  • Dimensions: 56.7 × 65.1 × 5.5 mm
  • Weight: 19 g (board only)
  • Interface: PCIe
  • Compatibility: Raspberry Pi 5 only
📦 What’s in the Box
  • Raspberry Pi AI HAT+ 2 board
  • Required mounting hardware
Unlock fast, private, and efficient AI directly on your Raspberry Pi. No cloud required. Just plug in, build, and deploy.

Jargon buster

Plain-language definitions for the technical terms used above.

TOPS
TOPS means trillions of operations per second, often used to describe AI accelerator performance. It helps compare whether a computing module is suited to lightweight image recognition or more demanding neural-network workloads.
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