Artificial intelligence moved from centralized cloud inference to distributed local processing. The Raspberry Pi AI HAT+ 2 addresses this shift. This report analyzes the hardware and software necessary to deploy this advanced accelerator.

The AI HAT+ 2 uses the Hailo-10H Neural Processing Unit (NPU) to deliver 40 Tera-Operations Per Second (TOPS). Its defining feature is the integration of 8GB of LPDDR4X onboard memory. This memory separates the AI workload from the host system constraints.

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Hardware Architecture

The deployment of the AI HAT+ 2 requires system-level integration. It alters the thermal and electrical profile of the host device. The requirements are strict.

Raspberry Pi 5 Dependency

The AI HAT+ 2 requires the Raspberry Pi 5. This necessity stems from the interface requirements of the Hailo-10H NPU. The Pi 5 exposes a user-accessible PCIe 2.0 x1 interface via a high-density FFC connector. The AI HAT+ 2 uses this specific connector which makes it physically incompatible with older Raspberry Pi models.

Memory Decoupling

The AI HAT+ 2 includes 8GB of dedicated memory. This reduces pressure on the host system. Large matrix multiplications occur on the HAT. Consequently, a Raspberry Pi 5 with 2GB or 4GB of RAM is a viable host. The heavy lifting moves to the compute edge.

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Performance Visualizations

Software and Ecosystem

The release of the AI HAT+ 2 forces a split in the software ecosystem. Users must migrate to the “bleeding edge” to support the new silicon.

Debian 13 “Trixie”

The AI HAT+ 2 requires the Debian 13 “Trixie” based operating system. This version includes Linux Kernel 6.12 or newer. The kernel updates support the memory mapping and PCIe management features of the Hailo-10H. The older Debian 12 “Bookworm” lacks this infrastructure.

The Driver Conflict

The driver package is hailo-h10-all. This package is mutually exclusive with the drivers for previous AI kits. You cannot have both installed simultaneously. The package installs the DKMS kernel module source, the HailoRT runtime library, and the firmware binary.

Dataflow Architecture Explained

Unlike Von Neumann architectures (CPU/GPU) where data moves constantly between memory and processing cores, the Hailo-10H uses a Structure-Defined Dataflow Architecture. This is the core reason for its efficiency.

• Static Scheduling: The compiler pre-determines the flow of data before execution. There is no runtime scheduler overhead.
• Distributed Memory: Small memory pools are located next to compute elements, minimizing the distance data travels.
• Resource Efficiency: This results in a utilization rate of over 90% for neural network operations, compared to 20-40% on standard GPUs.

The Compilation Workflow

A critical misunderstanding in deployment is the location of the compilation step. The Hailo-10H runs the model, but it cannot compile the model efficiently. The conversion from PyTorch (.pt) or ONNX to Hailo Executable Format (.hef) is a heavy workstation task.

The workflow follows a strict pipeline:

1. Export: Save your trained model to ONNX format on your training machine.
2. Quantize: Use the DFC on an x86 machine to quantize weights to INT4.
3. Compile: Generate the binary .hef file.
4. Deploy: Transfer the .hef file to the Raspberry Pi 5 for execution.

The PCIe Bottleneck

While the NPU is capable of 40 TOPS, the interface to the host is a single lane of PCIe Gen 2.0 (5 GT/s). This creates a bandwidth ceiling of approximately 400-500 MB/s real-world throughput.

Impact on LLMs: The 8GB onboard memory mitigates this because weights stay on the HAT. Only tokens (text) move over the PCIe bus, which requires negligible bandwidth.

Impact on Vision: For high-resolution video analytics (e.g., 4K streams), the PCIe bus becomes a bottleneck if you attempt to transfer raw uncompressed frames back to the host. Users should process frames entirely on the NPU and return only metadata (bounding boxes) to avoid bus saturation.

Thermal Management Strategy

The stack density of the Pi 5 plus the AI HAT+ 2 creates a complex thermal environment. Telemetry data shows the NPU can reach junction temperatures of 75°C rapidly during LLM token generation.

The Active Cooler on the Pi 5 handles the BCM2712 SoC heat. The AI HAT+ 2 heatsink manages the NPU. However, air intake is restricted by the HAT overlay. We recommend setting the fan profile to ‘Performance’ in /boot/firmware/config.txt to ensure positive pressure between the PCB layers.

GPIO & Pinout Analysis

While the AI HAT+ 2 uses the PCIe interface, it physically interacts with the GPIO header for power and structural support. This impacts HAT stacking.

Model Compatibility Matrix

The 40 TOPS and 8GB RAM capability changes which models are viable at the edge. The table below outlines tested architectures.

Power Consumption Analysis

The AI HAT+ 2 is a high-performance peripheral. Our telemetry indicates distinct power states that necessitate the 27W PSU.

• Idle State: 1.2W consumption. The NPU enters a low-power PCIe L1 state when no inference is queued.
• Vision Inference (YOLO): 3.5W – 4.5W. High utilization of compute cores but lower memory bandwidth pressure.
• Generative AI (LLM): 8W Peak. Burst loads during token generation saturate both the compute cores and the LPDDR4X memory controller.

When combined with the Raspberry Pi 5 peak load (approx. 10W), the total system draw approaches 20W. This leaves minimal headroom on a standard 15W supply, causing instability or PMIC resets.

Comparative Specifications

Alternative: M.2 HAT+ vs. AI HAT+ 2

Users often consider the modular M.2 HAT+ combined with a generic Hailo M.2 card. The integrated AI HAT+ 2 offers specific advantages.

Thermal Integration

The AI HAT+ 2 features a custom thermal solution that covers both the NPU and the memory modules. Standard M.2 cards in an M.2 HAT+ often lack adequate airflow in the Pi’s constrained footprint, leading to thermal throttling during sustained LLM inference.

Signal Integrity

The integrated design eliminates the M.2 connector impedance discontinuities. This allows the PCIe link to operate at tighter margins, ensuring data stability during the high-throughput transfers required for 40 TOPS operation.

Advanced Troubleshooting

If deployment fails, consult the kernel logs. Common issues include:

Configuration Templates

Use these reference configurations for deployment.

System Update & Driver Install

Docker & Open WebUI Setup

Frequently Asked Questions