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Verdict: The Raspberry Pi AI HAT+ 2 is a specialist accelerator for Raspberry Pi 5 projects that need private, local edge AI. Its Hailo-10H chip delivers 40 TOPS of INT4 inference and includes 8GB of dedicated memory, enabling supported small local language and vision-language models that the cheaper AI HAT+ does not support. At $200 for the board alone, however, it is difficult to justify for computer vision only, unrestricted local AI, training, or CUDA-based software.
It is most attractive for smart cameras, robotics, offline assistants, and compact vision-plus-language systems where Raspberry Pi compatibility, low-latency edge processing, and offline operation matter more than maximum performance or model flexibility.
What is the Raspberry Pi AI HAT+ 2?
The AI HAT+ 2 is a PCIe-connected add-on board for the Raspberry Pi 5. It is built around Hailo’s Hailo-10H neural-network accelerator and is designed for inference rather than model training.
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#1 Best Overall
- Hailo-10H AI accelerator delivering 40 TOPS (INT4) inferencing performance.
- Performance for computer vision models comparable to the Raspbery Pi AI HAT+ (26 TOPS).
- Runs generative AI models efficiently using 8GB on-board RAM.
- Fully integrated into Raspbery Pi’s camera software stack.
- Conforms to Raspbery Pi HAT+ specification.
The AI HAT+ 2 complies with the Raspberry Pi HAT+ mechanical and electrical specification. It includes a heatsink, 16mm stacking header, spacers, and screws. It does not include a Raspberry Pi 5, power supply, storage, camera, case, display, or operating-system media.
Key specifications
| Feature | AI HAT+ 2 |
|---|---|
| Accelerator | Hailo-10H NPU |
| Published inference performance | 40 TOPS at INT4 |
| Onboard memory | 8GB LPDDR4X dedicated to the accelerator |
| Host compatibility | Raspberry Pi 5 |
| Connection | Pi 5 PCIe interface |
| Intended workloads | Computer vision, supported LLMs, VLMs, speech-to-text, translation, and scene analysis |
| Ambient operating range | 0°C to 50°C |
| Official list price | $200, subject to region, tax, currency, and reseller pricing |
| Production commitment | At least January 2036 |
See the official product page and product brief for the manufacturer’s complete specifications.
What does 40 TOPS mean?
TOPS means tera-operations per second: a theoretical measure of how many trillion low-level operations an accelerator can perform. The AI HAT+ 2’s 40-TOPS figure is specified at INT4, a four-bit integer precision used for quantized inference.
That number is not a direct prediction of tokens per second, frames per second, or application latency. Performance also depends on the model architecture, supported operators, quantization, compiler efficiency, memory movement, input resolution, preprocessing, postprocessing, and work performed by the Raspberry Pi 5’s CPU.
Precision also makes comparisons easy to misread. The older AI HAT+ models are rated at 13 or 26 TOPS using INT8. Comparing 40 INT4 TOPS directly with 26 INT8 TOPS, a CUDA figure, or a GPU benchmark does not produce a meaningful universal ranking.
For conventional computer vision, Raspberry Pi says the AI HAT+ 2 performs broadly comparably to the 26-TOPS AI HAT+. The extra headline performance is therefore most relevant to the newer board’s generative-AI capabilities, not necessarily to every object-detection workload.
Why the 8GB onboard memory matters
The 8GB is not system RAM and does not turn a 4GB Raspberry Pi 5 into an 8GB computer. It is dedicated memory on the AI HAT+ 2 for accelerator workloads.
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Capacity is still not equivalent to usable model size. Memory must also accommodate runtime buffers, intermediate activations, the KV cache, context length, vision encoders, and multiple models. A six-billion-parameter model may fit under one quantization and context configuration but fail under another.
Which workloads are realistic?
Good use cases
- Smart cameras: object, person, vehicle, animal, or industrial-item detection.
- Robotics perception: detection, pose estimation, segmentation, and scene understanding.
- Privacy-sensitive monitoring: local analysis without sending camera footage to a cloud service.
- Offline document assistants: small, supported local models for question answering over private documents.
- Vision-language prototypes: camera input combined with short textual descriptions or responses.
- Embedded voice and translation projects: where a suitable Hailo-supported model is available.
- Educational and demonstration systems: compact experiments that combine Pi cameras, GPIO, and local inference.
Raspberry Pi integrates supported vision accelerators with its camera software ecosystem, including rpicam-apps and Picamera2. This makes the board particularly compelling when the project already depends on the Pi camera stack or GPIO-oriented application code.
Rank #2
- HIGH PERFORMANCE: Features 26 TOPS (Trillion Operations Per Second) AI acceleration capability through the Hailo AI Accelerator for advanced machine learning applications
- COMPATIBILITY: Specifically designed for the Raspberry Pi 5, connecting via PCIe interface for optimal data transfer and processing speeds
- COMPACT DESIGN: Measures 65mm x 56.5mm, offering a space-efficient solution while maintaining full functionality as an AI acceleration add-on board
- TEMPERATURE RANGE: Operates reliably in temperatures from 0°C to +50°C (32°F to 122°F), ensuring stable performance in various environments
- SEAMLESS INTEGRATION: Functions as a HAT (Hardware Attached on Top) add-on board, providing plug-and-play compatibility with Raspberry Pi ecosystem
Workloads that need caution
- Long-context conversations and large multimodal agents.
- Models above the approximate six-billion-parameter class.
- Arbitrary Hugging Face models or models without Hailo support.
- CUDA-dependent applications and TensorRT workflows.
- Full PyTorch training or serious local fine-tuning.
- High-resolution image generation.
- Several concurrent generative models.
- Applications dominated by tokenization, decoding, preprocessing, or CPU-side orchestration.
“Supports LLMs” means supported and appropriately compiled models. It does not mean that every model in Ollama, PyTorch, Hugging Face, or another desktop-oriented local-AI tool will run on the board.
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Installation and software requirements
A practical setup needs more than plugging the HAT into the Pi.
- Prepare the hardware: use a Raspberry Pi 5, install the supplied header and mounting hardware, and ensure the board is seated correctly on the PCIe connection.
- Use current Raspberry Pi OS software: Raspberry Pi OS must detect the accelerator, and the operating system, firmware, kernel, runtime, and model packages need to be compatible.
- Install the vision stack where required: this includes the Hailo runtime, supported model packages,
rpicam-apps, and/orPicamera2. - Install generative-AI components separately: LLM and VLM projects require Hailo’s generative-AI software stack, compatible model files, and any required conversion or compilation tools.
- Start with an official example: validate the accelerator with a known-supported model before attempting a community conversion or custom pipeline.
Use the current Raspberry Pi AI software documentation, AI HAT documentation, and the Hailo repositories. Package names and installation commands can change, so a fixed command sequence should not be treated as timeless.
Hardware you need
- Raspberry Pi 5.
- AI HAT+ 2.
- A suitable USB-C power supply with enough headroom for the Pi and peripherals.
- microSD, USB, or another boot medium.
- Active cooling for sustained Raspberry Pi 5 workloads.
- A Raspberry Pi camera for vision or VLM projects.
- A case or mounting solution that fits the stacked board, heatsink, and cables.
The board is designed to fit alongside the Raspberry Pi Active Cooler, but that does not guarantee compatibility with every enclosure. Check mechanical clearance before buying a case.
Practical limitations
Model support is the main constraint
The Hailo-10H is a specialized inference accelerator, not a general-purpose GPU. A model must be supported by Hailo’s runtime and compiler toolchain or converted successfully. Unsupported operators, unusual architectures, tokenizer requirements, precision constraints, or incompatible model formats can stop deployment before performance becomes relevant.
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The NPU does not perform every task in an AI application. The Pi 5 may still handle camera capture, resizing, tokenization, networking, storage, postprocessing, databases, and control logic. A fast accelerator cannot remove a bottleneck elsewhere in the application.
Memory is finite
The 8GB onboard memory enables supported local generative AI, but it is not an unlimited model pool. Context length and KV-cache growth can be especially important for chat systems. Vision-language workloads may also need memory for a vision encoder in addition to the language model.
Thermals and power matter during sustained use
Short demonstrations can hide thermal behavior. Sustained workloads should use appropriate Pi 5 cooling and power delivery, and the product brief’s 0°C-to-50°C ambient range should be kept in mind. Do not assume that a brief successful run represents continuous production performance.
PCIe is an opportunity cost
The AI HAT+ 2 occupies the Raspberry Pi 5’s PCIe connection. This can complicate projects that also require a PCIe NVMe HAT or another PCIe accessory. Storage may need to move to microSD or USB, or the project may require a compatible multiplexer or different hardware arrangement. Not every splitter or multiplexer will work reliably, and simultaneous AI-plus-NVMe operation should be verified for the exact hardware.
AI HAT+ 2 versus the original AI HAT+
| Feature | AI HAT+ | AI HAT+ 2 |
|---|---|---|
| Accelerator | Hailo-8L or Hailo-8 | Hailo-10H |
| Published rating | 13 or 26 TOPS, INT8 | 40 TOPS, INT4 |
| Dedicated onboard memory | No; uses Pi 5 memory | 8GB |
| Raspberry Pi-listed LLM/VLM support | Not supported in the comparison table | Supported categories |
| Best fit | Vision and robotics | Vision plus supported local generative AI |
| Official price signal | From $70 | $200 |
Choose the AI HAT+ when the project is mainly object detection, segmentation, pose estimation, or camera analytics. Raspberry Pi’s own comparison says the AI HAT+ 2’s computer-vision performance is broadly comparable to the 26-TOPS AI HAT+, making the cheaper board the more rational purchase for many vision-only builds.
Rank #3
- ⚡ PoE HAT for Raspberry Pi 5 CM5: PoE HAT F is a Power over Ethernet expansion board for Raspberry Pi 5 and CM5, supporting network connection and power input through one Ethernet cable.
- 🔌 802.3af/at PoE+ Support: This PoE+ HAT supports IEEE 802.3af/at network standard and works with compatible PoE power sourcing equipment for compact wired deployment projects.
- 🧊 Active Cooling Fan and Metal Heatsink: The PoE HAT with cooling fan includes a metal heatsink and high-speed active fan, helping improve heat dissipation and operating stability during long-term use.
- 🔋 5V and 12V Output Headers: Onboard 5V and 12V header outputs provide power options for external peripherals, with up to 25W total output under suitable PoE input and cooling conditions.
- 🧩 40-pin GPIO Stackable Header: Standard 40-pin GPIO stackable header fits Raspberry Pi 5 and CM5 expansion, allowing users to connect compatible HATs and custom project interfaces.
Pay for the AI HAT+ 2 when you specifically need its dedicated memory and supported LLM or VLM functionality.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.AI HAT+ 2 versus Jetson Orin Nano Super
| Priority | Better fit | Why |
|---|---|---|
| Existing Pi 5 project | AI HAT+ 2 | Preserves the Pi camera, GPIO, software, and mechanical ecosystem. |
| Vision-only Pi project | AI HAT+ | Much lower accessory cost for the relevant workload. |
| CUDA or TensorRT | Jetson Orin Nano Super | Designed around NVIDIA’s GPU software ecosystem. |
| Broader AI experimentation | Jetson Orin Nano Super | Generally more suitable for conventional GPU-oriented tooling. |
| Compact Pi-based edge device | AI HAT+ 2 | Natural fit for Pi cameras, GPIO, and existing Pi applications. |
NVIDIA lists the Jetson Orin Nano Super Developer Kit at $249 and advertises 67 AI TOPS after its software update. Those figures are not directly comparable with 40 INT4 TOPS on the AI HAT+ 2: the architectures, precision, memory arrangements, and software stacks differ.
The Jetson is the stronger starting point for an AI-first system that needs CUDA, TensorRT, or broader GPU-oriented model support. The AI HAT+ 2 is the better fit when the project is already a Raspberry Pi 5 product or prototype.
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How much does a complete system cost?
The $200 board price is only part of the purchase. A new build may also require a Raspberry Pi 5, power supply, cooling, storage, camera, and a compatible case. Raspberry Pi’s pricing announcement lists an 8GB Pi 5 at $80 as a pricing signal, but regional and later pricing should be checked before purchase.
Existing Pi 5 owners may need only the HAT, a cooling check, and a mounting solution. Buyers starting from zero should compare the complete system cost with a Jetson, an existing desktop, a mini PC, or a cloud service—not just the accessory price.
Troubleshooting common failures
The HAT is not detected
- Shut down the Pi and disconnect power.
- Reseat the HAT and inspect the PCIe connector, header, spacers, and screws.
- Update Raspberry Pi OS and firmware using the current official documentation.
- Reboot and inspect system logs for PCIe or Hailo detection messages.
- Disconnect other PCIe accessories and test again.
- Confirm that the AI packages match the installed OS, kernel, and runtime.
Power problems, incorrect assembly, outdated firmware, physical conflicts, and PCIe configuration issues are all plausible causes. Use the current Raspberry Pi and Hailo troubleshooting guidance rather than relying on commands written for an older release.
A model will not compile or load
Check for unsupported operators, an incorrect model format, incompatible quantization, insufficient memory, missing runtime components, or a compiler/runtime version mismatch. Begin with an official sample model, verify the exact supported model family, reduce model size or context length, and then consult the model-conversion documentation.
Performance is lower than expected
Check whether the published TOPS figure applies to the selected precision and whether the measurement includes only accelerator work. Also investigate camera resolution, CPU preprocessing and postprocessing, PCIe configuration, thermal throttling, concurrency, tokenization, and decoding. TOPS alone cannot explain end-to-end performance.
Who should buy it?
- Buy the AI HAT+ 2 if you own a Pi 5, need offline or private inference, and have confirmed that your target LLM, VLM, or vision models are supported by Hailo.
- Buy the cheaper AI HAT+ if your workload is computer vision only and the 26-TOPS model meets your throughput requirements.
- Choose Jetson Orin Nano Super if you are building an AI-first system and need CUDA, TensorRT, broader GPU tooling, or more flexible generative-AI experimentation.
- Choose neither if you need unrestricted model compatibility, large context windows, training, serious fine-tuning, desktop-class performance, or simultaneous PCIe NVMe without a carefully verified hardware arrangement.
Final verdict
The Raspberry Pi AI HAT+ 2 is a meaningful upgrade for one specific reason: it brings dedicated memory and supported generative-AI inference to the Raspberry Pi 5. That makes it far more capable for local LLM and VLM experiments than the original AI HAT+.
It is not a general-purpose GPU, a universal local-LLM device, or a training platform. Its value depends on confirmed Hailo model support, the Pi 5’s remaining CPU capacity, cooling, storage planning, and the cost of the complete system. For Pi-based edge AI with a camera, robotics hardware, or privacy requirements, it is compelling. For vision-only projects, the AI HAT+ is usually the better value; for CUDA-oriented or broader AI development, Jetson is the safer choice.
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