- Raspberry Pi AI HAT+ 2 allows Raspberry Pi 5 to run LLMs locally
- Hailo-10H accelerator delivers 40 TOPS of INT4 inference power
- PCIe interface enables high-bandwidth communication between the board and Raspberry Pi 5
Raspberry Pi has expanded its edge computing ambitions with the release of the AI HAT+ 2, an add-on board designed to bring generative AI workloads onto the Raspberry Pi 5.
Earlier AI HAT hardware focused almost entirely on computer vision acceleration, handling tasks such as object detection and scene segmentation.
The new board broadens that scope by supporting large language models and vision language models which run locally, without relying on cloud infrastructure or persistent network access.
Hardware changes that enable local language models
At the center of the upgrade is the Hailo-10H neural network accelerator, which delivers 40TOPS of INT4 inference performance.
Unlike its predecessor, the AI HAT+ 2 features 8GB of dedicated onboard memory, enabling larger models to run without consuming system RAM on the host Raspberry Pi.
This change allows direct execution of LLMs and VLMs on the device and maintains low latency and local data, which is a key requirement for many edge deployments.
Using a standard Raspberry Pi distro, users can install supported models and access them through familiar interfaces such as browser-based chat tools.
The AI HAT+ 2 connects to the Raspberry Pi 5 through the GPIO header and relies on the system’s PCIe interface for data transfer, which rules out compatibility with the Raspberry Pi 4.
This connection supports high-bandwidth data transfer between the accelerator and the host, which is essential for moving model inputs, outputs, and camera data efficiently.
Demonstrations include text-based question answering with Qwen2, code generation using Qwen2.5-Coder, basic translation tasks, and visual scene descriptions from live camera feeds.
These workloads rely on AI tools packaged to work within the Pi software stack, including containerized backends and local inference servers.
All processing occurs on the device, without external compute resources.
The supported models range from one to one and a half billion parameters, which is modest compared to cloud-based systems that operate at far larger scales.
These smaller LLMs target limited memory and power envelopes rather than broad, general-purpose knowledge.
To address this constraint, the AI HAT+ 2 supports fine-tuning methods such as Low-Rank Adaptation, which allows developers to customize models for narrow tasks while keeping most parameters unchanged.
Vision models can also be retrained using application-specific datasets through Hailo’s toolchain.
The AI HAT+ 2 is available for $130, placing it above earlier vision-focused accessories while offering similar computer vision throughput.
For workloads centered solely on image processing, the upgrade offers limited gains, as its appeal rests largely on local LLM execution and privacy-sensitive applications.
In practical terms, the hardware shows that generative AI on Raspberry Pi hardware is now feasible, although limited memory headroom and small model sizes remain an issue.
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